From: zulu Date: Tue, 3 Sep 2013 16:02:46 +0000 (+0200) Subject: 3 sep X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/these_gilles.git/commitdiff_plain/5efa829cb4570828a242e2baced8b215ea6029d5?ds=inline;hp=5468e1e9820093c08ad166ddd847123338479b2c 3 sep --- diff --git a/DOCS/blFilter_dagarwal2.pdf b/DOCS/blFilter_dagarwal2.pdf new file mode 100644 index 0000000..c1cec56 Binary files /dev/null and b/DOCS/blFilter_dagarwal2.pdf differ diff --git a/DOCS/convolution_soup_1412_GTC09.pdf b/DOCS/convolution_soup_1412_GTC09.pdf new file mode 100644 index 0000000..beb563d Binary files /dev/null and b/DOCS/convolution_soup_1412_GTC09.pdf differ diff --git a/DOCS/irfu-08-420.pdf b/DOCS/irfu-08-420.pdf new file mode 100644 index 0000000..58584d4 Binary files /dev/null and b/DOCS/irfu-08-420.pdf differ diff --git a/THESE/Chapters/chapter2/chapter2.tex b/THESE/Chapters/chapter2/chapter2.tex index 2e2e701..9788856 100644 --- a/THESE/Chapters/chapter2/chapter2.tex +++ b/THESE/Chapters/chapter2/chapter2.tex @@ -8,10 +8,10 @@ Ces dispositifs d'acquisition sont naturellement, et par essence, générateurs On peut dores et déjà avancer que la connaissance de l'origine d'une image et donc des propriétés des bruits associés qui en corrompent les informations, est un atout permettant de concevoir des techniques de filtrage adaptées à chaque situation. Toutefois, la recherche d'un filtre universel, bien qu'encore illusoire, n'est pas abandonnée, tant les besoins sont nombreux, divers et souvent complexes. \section{Modèle d'image bruitée} -On considère qu'une image observée est un ensemble de $N$ observations sur un domaine $\Omega$ à deux dimensions ($\Omega \subset \mathbb{Z}^2$). À chaque élément de $\Omega$, aussi appelé \textit{pixel}, est associé un indice unique $k \in [\![1;N]\!]$, une position $x_k=(i,j)_k \in\Omega$ et une valeur observée $v_k=v(i,j)_k$. +On considère qu'une image observée, de largeur $L$ pixels et de hauteur $H$ pixels, est un ensemble de $N=HL$ observations sur un domaine $\Omega$ à deux dimensions ($\Omega \subset \mathbb{Z}^2$). À chaque élément de $\Omega$, aussi appelé \textit{pixel}, est associé un indice unique $k \in [\![1;N]\!]$, une position $x_k=(i,j)_k \in\Omega$ et une valeur observée $v_k=v(i,j)_k$. La valeur observée peut, selon les cas, être de dimension $1$ pour les images représentées en niveaux de gris ou de dimension 3 pour les images couleur représentées au format RVB. Les dimensions supérieures, pour la représentation des images hyperspectrales n'est pas abordé. L'image observée peut ainsi être considérée comme un vecteur à $N$ éléments $\bar{v}= (v_k)_{k\in [\![1;N]\!]}$. -Les divers traitements appliqués aux images observées ont souvent pour but d'accéder aux informations contenues dans une image sous-jacente, débarrassée de toute perturbation, dont nous faisons l'hypothèse qu'elle partage le même support $\Omega$ et que nous notons $\bar{u}$. +Les divers traitements appliqués aux images observées ont souvent pour but d'accéder aux informations contenues dans une image sous-jacente, débarrassée de toute perturbation, dont nous faisons l'hypothèse qu'elle partage le même support $\Omega$ et que nous notons $\bar{u}$. L'estimation de $\bar{u}$ réalisée par ces traitements est notée $\widehat{\bar{u}} = (\widehat{u}_k)_{k\in [\![1;N]\!]}$. Le lien entre $\bar{u}$ et $\bar{v}$ peut être exprimé généralement par la relation $\bar{v}=\bar{u}+\sigma\epsilon$, où $\epsilon \in \mathbb{R}^N$ représente le modèle de perturbation appliquée à $\bar{u}$ et $\sigma$ représente la puissance de cette perturbation qui a mené à l'observation de $\bar{v}$. Dans le cas général, $\epsilon$ dépend de $\bar{u}$ et est caractérisé par la densité de probabilité (PDF pour probability density function) $p(v|u)$. @@ -60,11 +60,104 @@ Le bruit de grenaille est de type multiplicatif et suit une loi de Poisson. La P \[ p(v \mid u)=\mathrm{e}\frac{u^v}{v!}\] \section{Les techniques de réduction de bruit} -La très grande majorité des algorithmes de réduction de bruit fait l'hypothèse que la perturbation est de type gaussien, même si le développement des systèmes d'imagerie radar et médicale a poussé les chercheurs vers l'étude des bruits multiplicatifs du type \textit{speckle} ou \textit{Poisson}. -Un très grand nombre de travaux proposant des méthodes de réduction de ces bruits ont été menés, ainsi que beaucoup d'états de l'art et d'études comparatives de ces diverses techniques, que nous n'avons pas la prétention d'égaler. +La très grande majorité des algorithmes de réduction de bruit fait l'hypothèse que la perturbation est de type gaussien, même si le développement des systèmes d'imagerie radar et médicale a favorisé l'étude des bruits multiplicatifs du type \textit{speckle} ou \textit{Poisson}. +Un très grand nombre de travaux proposant des méthodes de réduction de ces bruits ont été menés, ainsi que beaucoup d'états de l'art et d'études comparatives de ces diverses techniques, que nous n'avons pas l'ambition d'égaler. -Les techniques et implémentations que nous aborderons dans le chapitre suivant sont celles qui ont un lien direct avec les travaux que nous avons menés. Nous -présenterons donc les principales classes d'algorithmes de réduction de bruit et les implémentations GPU qui leur ont été consacrées. +Nous nous focaliserons sur les techniques en lien avec les travaux que nous avons menés et qui ont donné lieu à des implémentations efficaces susceptibles de fournir des éléments opérationnels rapides pour le prétraitement des images. + +La figure \ref{fig-ny-noises} montre une image de synthèse issue de la base de test COIL \ref{adresse}, supposée sans bruit et qui sera considérée comme référence, ainsi que deux versions bruitées, respectivement avec un bruit gaussien d'écart type 25 et un bruit impulsionnel affectant 25\% des pixels. +\begin{figure} + \centering + \subfigure[Sans bruit]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/ny256.png}} + \subfigure[Bruit gaussien $\sigma=25$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/ny256_gauss25.png}} + \subfigure[Bruit impulsionnel 25\%]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/ny256_sap25.png}} + \caption{Images 256$\times$256 en niveau de gris 8 bits utilisées pour l'illustration des propriétés des filtres. a) l'image de référence non bruitée. b) l'image corrompue par un bruit gaussien d'écart type $\sigma=25$. c) l'image corrompue par un bruit impulsionnel à 25\%.} +\label{fig-ny-noises} +\end{figure} + +\subsection{Les opérateurs de base} +\subsubsection{Les algorithmes de voisinage} +L'opération la plus employée dans les procédés de traitement d'image est sans doute la convolution. Selon les valeurs affectées aux coefficients du masque, le filtrage par convolution permet de réaliser bon nombre de traitements comme la réduction de bruit par moyennage ou noyau gaussien ou encore la détection de contours. +Si la fonction définissant le masque de convolution est notée $h$, l'expression générale de la valeur estimée de pixel de coordonnées $(i,j)$ est donnée par +\begin{equation} +\widehat{u}(x, y) = \left(\bar{v} * h\right) = \sum_{(i < H)} \sum_{(j < L)}v(x-j, y-i)h(j,i) +\label{convoDef} +\end{equation} +Dans les applications les plus courantes, $h$ est à support borné et de forme carrée et l'on parle alors de la taille du masque pour évoquer la dimension du support. + La figure \ref{fig-ny-convo} présente les résultats de la convolution par deux masques \textit{moyenneurs} $h_3$ et $h_5$ de taille différentes, appliqués à l'image corrompue par un bruit gaussien : on voit la diminution des fluctuations mais aussi le flou apporté et qui rend les contours d'autant moins définis que la taille du masque est grande. La troisième image montre le résultat de la convolution de l'image de référence par un masque \textit{dérivateur} $h_{dx}$ selon l'axe horizontal. On y constate la mise en évidence, incomplète, des contours. +Les matrices définissant les masques sont les suivantes : + +\[h_3=\frac{1}{9}\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\end{bmatrix}, h_{25}=\frac{1}{25}\begin{bmatrix}1&1&1&1&1\\1&1&1&1&1\\1&1&1&1&1\\1&1&1&1&1\\1&1&1&1&1\end{bmatrix}, h_{dx}= \begin{bmatrix}0&0&0\\-1&1&0\\0&0&0\end{bmatrix}\] + +\begin{figure} + \centering + \subfigure[Moyenneur 3$\times$3]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/convo/ny256_gauss25_moy3.png}} + \subfigure[Moyenneur 5$\times$5]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/convo/ny256_gauss25_moy5.png}} + \subfigure[Dérivateur horizontal]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/convo/ny256_2dx.png}} +\caption{.} +\label{fig-ny-convo} +\end{figure} + +Le filtrage médian \ref{médian\_tukey} est également une opération très employée en prétraitement pour sa simplicité et ses propriétés de préservation des contours alliées à une capacité de réduction de bruit gaussien importante. +La valeur du niveau de gris de chaque pixel est remplacée par la médiane des niveaux de gris des pixels voisins. Un des intérêts de ce filtre réside dans le fait que la valeur filtrée est une des valeurs du voisinage, contrairement à ce qui se produit lors d'une convolution. Un autre est de bien filtrer les valeurs extrêmes et par conséquent de trouver naturellement son application dans la réduction du bruit impulsionnel. +Toutefois, la non-linéraité de cette technique et sa complexité n'en ont pas fait un filtre très utilisé jusqu'à ce que des implémentation efficaces soient proposées, en particulier le filtre à temps de calcul ``constant'' décrit dans \ref{medianO(1)}. Il est à noter que le filtrage médian est souvent appliqué en plusieurs passes de voisinage restreint. +La figure \ref{fig-ny-median} montre la réduction de bruit impulsionnel obtenu grâce au filtre médian, dans trois conditions distinctes : median 3$\times$3 en une ou deux passes, puis médian 5$\times$5. +\begin{figure} + \centering + \subfigure[Médian 3$\times$3 une passe]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/median/ny256_sap25_med3.png}} + \subfigure[Médian 3$\times$3 deux passes]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/median/ny256_sap25_med3x2.png}} + \subfigure[Médian 5$\times$5 une passe]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/median/ny256_sap25_med5.png}} +\caption{Réduction du bruit impulsionnel par filtre médian.} +\label{fig-ny-median} +\end{figure} + +Le filtre bilatéral \ref{bilatéral\_filter} est une composition d'opérations que l'on peut voir comme un filtre de convolution dont les coefficients ne dépendraient pas uniquement de la position du pixel courant par rapport au pixel central, mais également de la différence de leurs intensités (cas des images en niveaux de gris). +Si l'on note $\Omega_k$ le voisinage du pixel d'indice $k$, l'expression générale du niveau de gris estimé est donnée par +\[\widehat{u_k}=\displaystyle\frac{\sum_{p\in \Omega_k}\left(F_S(x_p, x_k)F_I(v_p, v_k)v_p\right)}{\sum_{p\in\Omega_k }\left(F_S(x_p, x_k)F_I(v_p, v_k)\right)} \] +où $F_S$ et $F_I$ sont les fonctions de pondération spatiale et d'intensité. Classiquement, $F_S$ et $F_I$ sont des gaussiennes de moyennes nulles et d'écarts type $\sigma_S$ et $\sigma_I$. +Ce filtre se prête également bien à une utilisation en plusieurs passes sans flouter les contours. Des approximations séparables du filtre bilatéral, comme celle proposée dans \ref{bilateral-sep}, permettent d'obtenir des vitesses d'exécution plus élevées que les versions standard. Une variante à temps de calcul constant à même été proposée en 2008 par Porikli \ref{dans bilateral-sep ref 1 porikli}. +Ce filtre permet un bon niveau de réduction de bruit gaussien, mais au prix d'un nombre de paramètres plus élevé à régler, ce qu'illustre la figure \ref{fig-ny-bilat} où le filtrage de la même image a été réalisé avec 9 combinaisons de $\sigma_S$ et $\sigma_I$. +\begin{figure} + \centering +\subfigure[$\sigma_S=1.0$ et $\sigma_I=0.1$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_1_01.png}} +\subfigure[$\sigma_S=1.0$ et $\sigma_I=0.5$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_1_05.png}} +\subfigure[$\sigma_S=1.0$ et $\sigma_I=1.0$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_1_1.png}}\\ +\subfigure[$\sigma_S=2.0$ et $\sigma_I=0.1$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_2_01.png}} +\subfigure[$\sigma_S=2.0$ et $\sigma_I=0.5$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_2_05.png}} +\subfigure[$\sigma_S=2.0$ et $\sigma_I=1.0$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_2_1.png}}\\ +\subfigure[$\sigma_S=5.0$ et $\sigma_I=0.1$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_5_01.png}} +\subfigure[$\sigma_S=5.0$ et $\sigma_I=0.5$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_5_05.png}} +\subfigure[$\sigma_S=5.0$ et $\sigma_I=1.0$]{\includegraphics[width=4cm]{/home/zulu/Documents/these_gilles/THESE/codes/bilat/ny_gauss25_bilat_5_1.png}}\\ +\caption{Réduction de bruit gaussien par filtrage bilatéral de voisinage 5$\times$5. $\sigma_S$ et $\sigma_I$ sont les écarts type des fonctions gaussiennes de pondération spatiale et d'intensité.} +\label{fig-ny-bilat} +\end{figure} + + +Beaucoup d'autres algorithmes basés sur des moyennes locales efféctuées sur des voisinages de formes diverses, variables et/ou adaptatives afin de sélectionner le plus finement possible les pixels pris en compte dans le calcul de la valeur filtrée. +Le principal défaut de ces techniques dites de réduction de variance est de générer des aplats dans les zones homogènes et des marches d'escalier dans les zones de transition douce (staircase effect), ces dernières pouvant être considérablement atténuées comme il a été montré dans \ref{staircase-effect}. + +\subsubsection{Les algorithmes par dictionnaire} +Il s'agit ici de décrire l'image à débruiter en utilisant une base de fonctions permettant de décomposer l'image en une combinaison linéaire des éléments de cette base. Les bases les plus employées sont les ondelettes \ref{mallat2009-deladallep15, daubechie} ainsi que les fonctions sinusoïdales (DCT \ref{irfu}). Les éléments de la base peuvent être prédéterminés ou bien calculés à partir des données de l'image, par exemple en s'appuyant sur une analyse en composantes principales \ref{Aharon-2006 deladallep67}. + +\subsection{Les techniques avancées} +Les techniques de réduction de bruit les plus efficaces sont aujourd'hui celles qui reposent sur les propriétés d'auto-similarité ds images, on les appelles aussi les techniques par patchs. L'idée principale est, comme pour les techniques classiques à base de de voisinage, de rechercher un ensemble de pixels pertinents et comparables afin d'en faire une moyenne. Cependant, dans le cas des techniques à patchs, la recherche de cet ensemble ne se limite pas à un voisinage du pixel central, mais fait l'hypothèse qu'il existe des zones semblables au voisinage du pixel central, réparties dans l'image et pas nécessairement immédiatement contigues. +Le moyennage s'effectue alors sur l'ensemble des ces zones identifiées. +L'algorithme des Non-Local Means (NL-means \ref{nl-means}) fut le premier de cette lignée à être proposé, mais bien d'autres suivirent comme le BM3D et ses variantes qui représentent actuellement l'état de l'art en terme de qualité de débruitage \ref{bm3D}. + Les différences entre ces algorithmes résident essentiellement dans la méthode de recherche et d'identification des patchs similaires, incluant la possiblité de forme et taille variables. Une telle recherche est d'autant plus coûteuse en temps de calcul qu'elle est effectuée sur une zone étendue autour du pixel central et cela représente le principal inconvénient de ces techniques qui peuvent présenter des temps d'exécution prohibitifs dans l'optique d'un traitement en temps réel. + +\section{Les implémentations GPU des algorithmes de filtrage} +Le fabricant de processeurs graphiques Nvidia, seul type d'équipements dont nous disposons, fournit des implémentations performantes de certains prétraitements et algorithmes de filtrage. +%TODO +%Ajouter qq mots sur FFT, DCT utilisés dans irfu et que nous n'avons pas cherché à améliorer. +C'est en particulier le cas de la convolution qui a fait l'objet d'une étude et d'une optimisation poussées pour déterminer la combinaison de solutions apportant la plus grande vitesse d'exécution \ref{convolution-soup-gtc09}. L'étude a testé 16 versions distinctes, chacune présentant une optimisation particulière quant-à l'organisation de la grille de calcul, aux types de transferts entre l'hôte et le GPU ainsi qu'au types de mémoire employé pour le calcul sur le GPU. Les résultats montrent que l'emploi de texture comme mémoire principale pour le stockage des images à traiter apporte un gain d'environ 50\% par rapport à l'utilisation de la mémoire globale. Par ailleurs, les transactions par paquets de 128 bits apportent également une amélioration sensible, ainsi que l'emploi de la mémoire partagée comme zone de travail pour le calcul des valeurs de sortie. Le traitement de référence effectué pour les mesures est la convolution générique (non séparable) d'une image 8 bits de 2048$\times$2048 pixels par un masque de convolution de 5$\times$5 pixels, expression que l'on raccourcira déronavant en \textit{convolution 5$\times$5}. +Le meilleur résultat obtenu dans les conditions détaillées précédemment, sur architecture GT200 (carte GTX280) est de 1.4~ms pour le calcul, ce qui réalise un débit global de 945~MP/s lorsque l'on prend en compte les temps de transfert aller et retour des images (1.5~ms d'après nos mesures). +Nous continuerons d'utiliser cette mesure de débit en \textit{Pixels par seconde} pour toutes les évaluations à venir ; elle permet en particulier de fournir des valeurs de performance indépendantes de la taille des images soumises au traitement. + +Le filtre médian n'a pas fait l'objet d'autant de publications, peut-être en raison des implémentations CPU performantes et génériques que l'on a déjà évoquées \ref{median0(1)}. Néanmoins, une bibliothèque commerciale (LibJacket, ArrayFire) en propose une implémentation GPU dont nous avons pu mesurer les performances pour un masque de 3$\times$3 et qui est également prise comme référence par Sanchez \textit{et al.} pour évaluer les performances de leur propre implémentation appelée PCMF \ref{median sanchez x2}. Sur architecture GT200 (GTX260), les performances maximales de ces deux versions sont obtenues pour un masque de 3$\times$3 pixels avec respectivement 175~MP/s pour libJacket et 60~MP/s pour PCMF. +La figure \ref{fig-compare-jacket-pcmf}, tirée de \ref{median sanchez}, montre que le débit permis par la libJacket décroit très vite avec la taille du masque pour passer à 30~MP/s dès la taille 5$\times$5, alors que le PCMF décroit linéairement jusqu'à la taille 11$\times$11 où il permet encore de traiter quelque 40~MP/s. +Plus récemment, Sanchez \textit{et al.} ont actualisé leurs mesures sur architecture Fermi (GPU C2075) en comparant leur PCMF à la version ré-écrite en C de libJacket, nommée ArrayFire. Les courbes sont celles de la figure \ref{fig-compare-arrayfire-pcmf}, où l'on constate que les variations selon la taille du masque demeurent comparables, avec toutefois des valeurs de débit augmentées, avec près de 185~MP/s pour ArrayFire et 82~MP/s pour PCMF. Il faut aussi noter que certains codes sont plus performants sur l'ancienne architecture GT200 que sur la plus récente Fermi ; c'est le cas pour l'implémentation du médian incluse dans la bibliothèque ArrayFire et nous reviendrons sur les raisons de cette perte de performances constatée au passage à une architecture plus récente dans le chapitre consacré à notre implémentation du filtre médian. + +Le filtre bilatéral a été plus abordé et un certain nombre de publications font état d'implémentations véritablement rapides. Il est néanmoins parfois difficile de les comparer sans disposer des codes sources, en raison de conditions de test très variables, en particulier en ce qui concerne le modèle de GPU et la taille du masque . Ceci étant précisé, on peut prendre comme référence initiale la version proposée par Nvidia dans le SDK CUDA et nommée ``ImageDenoising''. Elle permet d'exécuter sur GPU GTX480 un filtre bilatéral 7$\times$7 sur une image, déjà en mémoire GPU, de 1~MPixels en 0.411~ms. \section{Les techniques de segmentation} @@ -123,20 +216,32 @@ Nous pouvons retenir que les premières d'entre elles, qui n'étaient pas spéci L'extension a rapidement été faite aux images numériques en ajoutant l'intensité des pixels au vecteur des paramètres pris en compte dans l'évaluation du poids des liens. D'autres critères de simplification ont aussi été élaborés, avec pour ambition de toujours mieux prendre en compte les caractéristiques structurelles globales des images pour prétendre à une segmentation qui conduise à une meilleure perception conceptuelle. Le principe général des solutions actuelles est proche de l'analyse en composantes principales appliquée à une matrice de similarité qui traduit les liens entre les segments. -On peut citer, par ordre chronologique, les méthodes reposant sur le \textit{graphe optimal} de Wu et Leahy \ref{wulealy_1993} et plus récemment \ref{cf_notes x5}. Le principal point faible de ces techniques réside essentiellement dans la difficulté à trouver un compromis acceptable entre identification de structures globales et préservation des éléments de détails. Cela se traduit dans la pratique par un ensemble de paramètres à régler pour chaque type de segmentation à effectuer. +Pour des images en niveaux de gris, l'expression générale des éléments $w_{ij}$ de la matrice de similarité $W$ est : +\[w_{ij} = +\begin{cases} +\mathrm{e}^{\|v_i-v_j\|^2/\sigma_v^2}\mathrm{e}^{\|x_i-x_j\|^2/\sigma_x^2} & \text{si $\|x_i-x_j\| 1 + error(['Input image A must be a double precision ',... + 'matrix of size NxMx1 or NxMx3 on the closed ',... + 'interval [0,1].']); +end + +% Verify bilateral filter window size. +if ~exist('w','var') || isempty(w) || ... + numel(w) ~= 1 || w < 1 + w = 5; +end +w = ceil(w); + +% Verify bilateral filter standard deviations. +if ~exist('sigma','var') || isempty(sigma) || ... + numel(sigma) ~= 2 || sigma(1) <= 0 || sigma(2) <= 0 + sigma = [3 0.1]; +end + +% Apply either grayscale or color bilateral filtering. +if size(A,3) == 1 + B = bfltGray(A,w,sigma(1),sigma(2)); +else + B = bfltColor(A,w,sigma(1),sigma(2)); +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Implements bilateral filtering for grayscale images. +function B = bfltGray(A,w,sigma_d,sigma_r) + +% Pre-compute Gaussian distance weights. +[X,Y] = meshgrid(-w:w,-w:w); +G = exp(-(X.^2+Y.^2)/(2*sigma_d^2)); + +% Create waitbar. +h = waitbar(0,'Applying bilateral filter...'); +set(h,'Name','Bilateral Filter Progress'); + +% Apply bilateral filter. +dim = size(A); +B = zeros(dim); +for i = 1:dim(1) + for j = 1:dim(2) + + % Extract local region. + iMin = max(i-w,1); + iMax = min(i+w,dim(1)); + jMin = max(j-w,1); + jMax = min(j+w,dim(2)); + I = A(iMin:iMax,jMin:jMax); + + % Compute Gaussian intensity weights. + H = exp(-(I-A(i,j)).^2/(2*sigma_r^2)); + + % Calculate bilateral filter response. + F = H.*G((iMin:iMax)-i+w+1,(jMin:jMax)-j+w+1); + B(i,j) = sum(F(:).*I(:))/sum(F(:)); + + end + waitbar(i/dim(1)); +end + +% Close waitbar. +close(h); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Implements bilateral filter for color images. +function B = bfltColor(A,w,sigma_d,sigma_r) + +% Convert input sRGB image to CIELab color space. +if exist('applycform','file') + A = applycform(A,makecform('srgb2lab')); +else + A = colorspace('Lab<-RGB',A); +end + +% Pre-compute Gaussian domain weights. +[X,Y] = meshgrid(-w:w,-w:w); +G = exp(-(X.^2+Y.^2)/(2*sigma_d^2)); + +% Rescale range variance (using maximum luminance). +sigma_r = 100*sigma_r; + +% Create waitbar. +h = waitbar(0,'Applying bilateral filter...'); +set(h,'Name','Bilateral Filter Progress'); + +% Apply bilateral filter. +dim = size(A); +B = zeros(dim); +for i = 1:dim(1) + for j = 1:dim(2) + + % Extract local region. + iMin = max(i-w,1); + iMax = min(i+w,dim(1)); + jMin = max(j-w,1); + jMax = min(j+w,dim(2)); + I = A(iMin:iMax,jMin:jMax,:); + + % Compute Gaussian range weights. + dL = I(:,:,1)-A(i,j,1); + da = I(:,:,2)-A(i,j,2); + db = I(:,:,3)-A(i,j,3); + H = exp(-(dL.^2+da.^2+db.^2)/(2*sigma_r^2)); + + % Calculate bilateral filter response. + F = H.*G((iMin:iMax)-i+w+1,(jMin:jMax)-j+w+1); + norm_F = sum(F(:)); + B(i,j,1) = sum(sum(F.*I(:,:,1)))/norm_F; + B(i,j,2) = sum(sum(F.*I(:,:,2)))/norm_F; + B(i,j,3) = sum(sum(F.*I(:,:,3)))/norm_F; + + end + waitbar(i/dim(1)); +end + +% Convert filtered image back to sRGB color space. +if exist('applycform','file') + B = applycform(B,makecform('lab2srgb')); +else + B = colorspace('RGB<-Lab',B); +end + +% Close waitbar. +close(h); \ No newline at end of file diff --git a/THESE/codes/bilat/Bilateral Filtering/cartoon.m b/THESE/codes/bilat/Bilateral Filtering/cartoon.m new file mode 100644 index 0000000..51620b2 --- /dev/null +++ b/THESE/codes/bilat/Bilateral Filtering/cartoon.m @@ -0,0 +1,84 @@ +function C = cartoon(A) + +% CARTOON Image abstraction using bilateral filtering. +% This function uses the bilateral filter to abstract +% an image following the method outlined in: +% +% Holger Winnemoller, Sven C. Olsen, and Bruce Gooch. +% Real-Time Video Abstraction. In Proceedings of ACM +% SIGGRAPH, 2006. +% +% C = cartoon(A) modifies the color image A to have a +% cartoon-like appearance. A must be a double precision +% matrix of size NxMx3 with normalized values in the +% closed interval [0,1]. Default filtering parameters +% are defined in CARTOON.M. +% +% Douglas R. Lanman, Brown University, September 2006. +% dlanman@brown.edu, http://mesh.brown.edu/dlanman + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Set the image abstraction parameters. + +% Set bilateral filter parameters. +w = 5; % bilateral filter half-width +sigma = [3 0.1]; % bilateral filter standard deviations + +% Set image abstraction paramters. +max_gradient = 0.2; % maximum gradient (for edges) +sharpness_levels = [3 14]; % soft quantization sharpness +quant_levels = 8; % number of quantization levels +min_edge_strength = 0.3; % minimum gradient (for edges) + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +% Verify that the input image exists and is valid. +if ~exist('A','var') || isempty(A) + error('Input image A is undefined or invalid.'); +end +if ~isfloat(A) || size(A,3) ~= 3 || ... + min(A(:)) < 0 || max(A(:)) > 1 + error(['Input image A must be a double precision ',... + 'matrix of size NxMx3 on the closed ',... + 'interval [0,1].']); +end + +% Apply bilateral filter to input image. +B = bfilter2(A,w,sigma); + +% Convert sRGB image to CIELab color space. +if exist('applycform','file') + B = applycform(B,makecform('srgb2lab')); +else + B = colorspace('Lab<-RGB',B); +end + +% Determine gradient magnitude of luminance. +[GX,GY] = gradient(B(:,:,1)/100); +G = sqrt(GX.^2+GY.^2); +G(G>max_gradient) = max_gradient; +G = G/max_gradient; + +% Create a simple edge map using the gradient magnitudes. +E = G; E(E= 3 + Image = cat(3,varargin{:}); +else + error('Invalid number of input arguments.'); +end + +FlipDims = (size(Image,3) == 1); + +if FlipDims, Image = permute(Image,[1,3,2]); end +if ~isa(Image,'double'), Image = double(Image)/255; end +if size(Image,3) ~= 3, error('Invalid input size.'); end + +SrcT = gettransform(SrcSpace); +DestT = gettransform(DestSpace); + +if ~ischar(SrcT) & ~ischar(DestT) + % Both source and destination transforms are affine, so they + % can be composed into one affine operation + T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)]; + Temp = zeros(size(Image)); + Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10); + Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11); + Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12); + Image = Temp; +elseif ~ischar(DestT) + Image = rgb(Image,SrcSpace); + Temp = zeros(size(Image)); + Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10); + Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11); + Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12); + Image = Temp; +else + Image = feval(DestT,Image,SrcSpace); +end + +%%% Output format %%% +if nargout > 1 + varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)}; +else + if FlipDims, Image = permute(Image,[1,3,2]); end + varargout = {Image}; +end + +return; + + +function [SrcSpace,DestSpace] = parse(Str) +% Parse conversion argument + +if isstr(Str) + Str = lower(strrep(strrep(Str,'-',''),' ','')); + k = find(Str == '>'); + + if length(k) == 1 % Interpret the form 'src->dest' + SrcSpace = Str(1:k-1); + DestSpace = Str(k+1:end); + else + k = find(Str == '<'); + + if length(k) == 1 % Interpret the form 'dest<-src' + DestSpace = Str(1:k-1); + SrcSpace = Str(k+1:end); + else + error(['Invalid conversion, ''',Str,'''.']); + end + end + + SrcSpace = alias(SrcSpace); + DestSpace = alias(DestSpace); +else + SrcSpace = 1; % No source pre-transform + DestSpace = Conversion; + if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end +end +return; + + +function Space = alias(Space) +Space = strrep(Space,'cie',''); + +if isempty(Space) + Space = 'rgb'; +end + +switch Space +case {'ycbcr','ycc'} + Space = 'ycbcr'; +case {'hsv','hsb'} + Space = 'hsv'; +case {'hsl','hsi','hls'} + Space = 'hsl'; +case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'} + return; +end +return; + + +function T = gettransform(Space) +% Get a colorspace transform: either a matrix describing an affine transform, +% or a string referring to a conversion subroutine +switch Space +case 'ypbpr' + T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0]; +case 'yuv' + % R'G'B' to NTSC/PAL YUV + % Wikipedia: http://en.wikipedia.org/wiki/YUV + T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0]; +case 'ydbdr' + % R'G'B' to SECAM YDbDr + % Wikipedia: http://en.wikipedia.org/wiki/YDbDr + T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0]; +case 'yiq' + % R'G'B' in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591]; + % Wikipedia: http://en.wikipedia.org/wiki/YIQ + T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0]; +case 'ycbcr' + % R'G'B' (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr + % Wikipedia: http://en.wikipedia.org/wiki/YCbCr + % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion + T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128]; +case 'jpegycbcr' + % Wikipedia: http://en.wikipedia.org/wiki/YCbCr + T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255; +case {'rgb','xyz','hsv','hsl','lab','luv','lch'} + T = Space; +otherwise + error(['Unknown color space, ''',Space,'''.']); +end +return; + + +function Image = rgb(Image,SrcSpace) +% Convert to Rec. 709 R'G'B' from 'SrcSpace' +switch SrcSpace +case 'rgb' + return; +case 'hsv' + % Convert HSV to R'G'B' + Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1)); +case 'hsl' + % Convert HSL to R'G'B' + L = Image(:,:,3); + Delta = Image(:,:,2).*min(L,1-L); + Image = huetorgb(L-Delta,L+Delta,Image(:,:,1)); +case {'xyz','lab','luv','lch'} + % Convert to CIE XYZ + Image = xyz(Image,SrcSpace); + % Convert XYZ to RGB + T = [3.240479,-1.53715,-0.498535;-0.969256,1.875992,0.041556;0.055648,-0.204043,1.057311]; + R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3); % R + G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3); % G + B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3); % B + % Desaturate and rescale to constrain resulting RGB values to [0,1] + AddWhite = -min(min(min(R,G),B),0); + Scale = max(max(max(R,G),B)+AddWhite,1); + R = (R + AddWhite)./Scale; + G = (G + AddWhite)./Scale; + B = (B + AddWhite)./Scale; + % Apply gamma correction to convert RGB to Rec. 709 R'G'B' + Image(:,:,1) = gammacorrection(R); % R' + Image(:,:,2) = gammacorrection(G); % G' + Image(:,:,3) = gammacorrection(B); % B' +otherwise % Conversion is through an affine transform + T = gettransform(SrcSpace); + temp = inv(T(:,1:3)); + T = [temp,-temp*T(:,4)]; + R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10); + G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11); + B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12); + AddWhite = -min(min(min(R,G),B),0); + Scale = max(max(max(R,G),B)+AddWhite,1); + R = (R + AddWhite)./Scale; + G = (G + AddWhite)./Scale; + B = (B + AddWhite)./Scale; + Image(:,:,1) = R; + Image(:,:,2) = G; + Image(:,:,3) = B; +end + +% Clip to [0,1] +Image = min(max(Image,0),1); +return; + + +function Image = xyz(Image,SrcSpace) +% Convert to CIE XYZ from 'SrcSpace' +WhitePoint = [0.950456,1,1.088754]; + +switch SrcSpace +case 'xyz' + return; +case 'luv' + % Convert CIE L*uv to XYZ + WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); + WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); + L = Image(:,:,1); + Y = (L + 16)/116; + Y = invf(Y)*WhitePoint(2); + U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU; + V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV; + Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V); % X + Image(:,:,2) = Y; % Y + Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V); % Z +case {'lab','lch'} + Image = lab(Image,SrcSpace); + % Convert CIE L*ab to XYZ + fY = (Image(:,:,1) + 16)/116; + fX = fY + Image(:,:,2)/500; + fZ = fY - Image(:,:,3)/200; + Image(:,:,1) = WhitePoint(1)*invf(fX); % X + Image(:,:,2) = WhitePoint(2)*invf(fY); % Y + Image(:,:,3) = WhitePoint(3)*invf(fZ); % Z +otherwise % Convert from some gamma-corrected space + % Convert to Rec. 701 R'G'B' + Image = rgb(Image,SrcSpace); + % Undo gamma correction + R = invgammacorrection(Image(:,:,1)); + G = invgammacorrection(Image(:,:,2)); + B = invgammacorrection(Image(:,:,3)); + % Convert RGB to XYZ + T = inv([3.240479,-1.53715,-0.498535;-0.969256,1.875992,0.041556;0.055648,-0.204043,1.057311]); + Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B; % X + Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B; % Y + Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B; % Z +end +return; + + +function Image = hsv(Image,SrcSpace) +% Convert to HSV +Image = rgb(Image,SrcSpace); +V = max(Image,[],3); +S = (V - min(Image,[],3))./(V + (V == 0)); +Image(:,:,1) = rgbtohue(Image); +Image(:,:,2) = S; +Image(:,:,3) = V; +return; + + +function Image = hsl(Image,SrcSpace) +% Convert to HSL +switch SrcSpace +case 'hsv' + % Convert HSV to HSL + MaxVal = Image(:,:,3); + MinVal = (1 - Image(:,:,2)).*MaxVal; + L = 0.5*(MaxVal + MinVal); + temp = min(L,1-L); + Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0)); + Image(:,:,3) = L; +otherwise + Image = rgb(Image,SrcSpace); % Convert to Rec. 701 R'G'B' + % Convert R'G'B' to HSL + MinVal = min(Image,[],3); + MaxVal = max(Image,[],3); + L = 0.5*(MaxVal + MinVal); + temp = min(L,1-L); + S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0)); + Image(:,:,1) = rgbtohue(Image); + Image(:,:,2) = S; + Image(:,:,3) = L; +end +return; + + +function Image = lab(Image,SrcSpace) +% Convert to CIE L*a*b* (CIELAB) +WhitePoint = [0.950456,1,1.088754]; + +switch SrcSpace +case 'lab' + return; +case 'lch' + % Convert CIE L*CH to CIE L*ab + C = Image(:,:,2); + Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C; % a* + Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C; % b* +otherwise + Image = xyz(Image,SrcSpace); % Convert to XYZ + % Convert XYZ to CIE L*a*b* + X = Image(:,:,1)/WhitePoint(1); + Y = Image(:,:,2)/WhitePoint(2); + Z = Image(:,:,3)/WhitePoint(3); + fX = f(X); + fY = f(Y); + fZ = f(Z); + Image(:,:,1) = 116*fY - 16; % L* + Image(:,:,2) = 500*(fX - fY); % a* + Image(:,:,3) = 200*(fY - fZ); % b* +end +return; + + +function Image = luv(Image,SrcSpace) +% Convert to CIE L*u*v* (CIELUV) +WhitePoint = [0.950456,1,1.088754]; +WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); +WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3)); + +Image = xyz(Image,SrcSpace); % Convert to XYZ +U = (4*Image(:,:,1))./(Image(:,:,1) + 15*Image(:,:,2) + 3*Image(:,:,3)); +V = (9*Image(:,:,2))./(Image(:,:,1) + 15*Image(:,:,2) + 3*Image(:,:,3)); +Y = Image(:,:,2)/WhitePoint(2); +L = 116*f(Y) - 16; +Image(:,:,1) = L; % L* +Image(:,:,2) = 13*L.*(U - WhitePointU); % u* +Image(:,:,3) = 13*L.*(V - WhitePointV); % v* +return; + + +function Image = lch(Image,SrcSpace) +% Convert to CIE L*ch +Image = lab(Image,SrcSpace); % Convert to CIE L*ab +H = atan2(Image(:,:,3),Image(:,:,2)); +H = H*180/pi + 360*(H < 0); +Image(:,:,2) = sqrt(Image(:,:,2).^2 + Image(:,:,3).^2); % C +Image(:,:,3) = H; % H +return; + + +function Image = huetorgb(m0,m2,H) +% Convert HSV or HSL hue to RGB +N = size(H); +H = min(max(H(:),0),360)/60; +m0 = m0(:); +m2 = m2(:); +F = H - round(H/2)*2; +M = [m0, m0 + (m2-m0).*abs(F), m2]; +Num = length(m0); +j = [2 1 0;1 2 0;0 2 1;0 1 2;1 0 2;2 0 1;2 1 0]*Num; +k = floor(H) + 1; +Image = reshape([M(j(k,1)+(1:Num).'),M(j(k,2)+(1:Num).'),M(j(k,3)+(1:Num).')],[N,3]); +return; + + +function H = rgbtohue(Image) +% Convert RGB to HSV or HSL hue +[M,i] = sort(Image,3); +i = i(:,:,3); +Delta = M(:,:,3) - M(:,:,1); +Delta = Delta + (Delta == 0); +R = Image(:,:,1); +G = Image(:,:,2); +B = Image(:,:,3); +H = zeros(size(R)); +k = (i == 1); +H(k) = (G(k) - B(k))./Delta(k); +k = (i == 2); +H(k) = 2 + (B(k) - R(k))./Delta(k); +k = (i == 3); +H(k) = 4 + (R(k) - G(k))./Delta(k); +H = 60*H + 360*(H < 0); +H(Delta == 0) = nan; +return; + + +function Rp = gammacorrection(R) +Rp = real(1.099*R.^0.45 - 0.099); +i = (R < 0.018); +Rp(i) = 4.5138*R(i); +return; + + +function R = invgammacorrection(Rp) +R = real(((Rp + 0.099)/1.099).^(1/0.45)); +i = (R < 0.018); +R(i) = Rp(i)/4.5138; +return; + + +function fY = f(Y) +fY = real(Y.^(1/3)); +i = (Y < 0.008856); +fY(i) = Y(i)*(841/108) + (4/29); +return; + + +function Y = invf(fY) +Y = fY.^3; +i = (Y < 0.008856); +Y(i) = (fY(i) - 4/29)*(108/841); +return; diff --git a/THESE/codes/bilat/Bilateral Filtering/einstein.jpg b/THESE/codes/bilat/Bilateral Filtering/einstein.jpg new file mode 100644 index 0000000..582cf94 Binary files /dev/null and b/THESE/codes/bilat/Bilateral Filtering/einstein.jpg differ diff --git a/THESE/codes/bilat/Bilateral Filtering/mandrill.jpg b/THESE/codes/bilat/Bilateral Filtering/mandrill.jpg new file mode 100644 index 0000000..350d8b9 Binary files /dev/null and b/THESE/codes/bilat/Bilateral Filtering/mandrill.jpg differ diff --git a/THESE/codes/bilat/Bilateral Filtering/runDemo.m b/THESE/codes/bilat/Bilateral Filtering/runDemo.m new file mode 100644 index 0000000..b5955ab --- /dev/null +++ b/THESE/codes/bilat/Bilateral Filtering/runDemo.m @@ -0,0 +1,64 @@ + +% RUNDEMO Illustrates the use of BFILTER2 and CARTOON. +% This demo shows typical usage for the bilateral +% filter implemented by BFILTER2. The application +% of bilateral filtering to image abstraction is +% demonstrated by the CARTOON function. +% +% Douglas R. Lanman, Brown University, September 2006. +% dlanman@brown.edu, http://mesh.brown.edu/dlanman + + +% Load test images. +% Note: Must be double precision in the interval [0,1]. +img1 = double(imread('einstein.jpg'))/255; +img2 = double(imread('mandrill.jpg'))/255; +img3 = double(imread('academy.jpg'))/255; + +% Introduce AWGN into test images. +% Note: This will show the benefit of bilateral filtering. +img1 = img1+0.03*randn(size(img1)); +img2 = img2+0.03*randn(size(img2)); +img1(img1<0) = 0; img1(img1>1) = 1; +img2(img2<0) = 0; img2(img2>1) = 1; + +% Set bilateral filter parameters. +w = 5; % bilateral filter half-width +sigma = [3 0.1]; % bilateral filter standard deviations + +% Apply bilateral filter to each image. +bflt_img1 = bfilter2(img1,w,sigma); +bflt_img2 = bfilter2(img2,w,sigma); + +% Display grayscale input image and filtered output. +figure(1); clf; +set(gcf,'Name','Grayscale Bilateral Filtering Results'); +subplot(1,2,1); imagesc(img1); +axis image; colormap gray; +title('Input Image'); +subplot(1,2,2); imagesc(bflt_img1); +axis image; colormap gray; +title('Result of Bilateral Filtering'); + +% Display color input image and filtered output. +figure(2); clf; +set(gcf,'Name','Color Bilateral Filtering Results'); +subplot(1,2,1); imagesc(img2); +axis image; colormap gray; +title('Input Image'); +subplot(1,2,2); imagesc(bflt_img2); +axis image; title('Result of Bilateral Filtering'); +drawnow; + +% Apply bilateral filter for a "cartoon" effect. +cartoon_img3 = cartoon(img3); + +% Display color input image and abstracted output. +figure(3); clf; +set(gcf,'Name','Image Abstraction Input'); +imagesc(img3); axis image; +title('Input Image'); +figure(4); clf; +set(gcf,'Name','Result of Image Abstraction'); +imagesc(cartoon_img3); axis image; +title('Abstracted Image'); \ No newline at end of file diff --git a/THESE/codes/bilat/bfilter2.zip b/THESE/codes/bilat/bfilter2.zip new file mode 100644 index 0000000..3b30cd8 Binary files /dev/null and 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/dev/null and b/THESE/codes/snake/gvf_dist_v4.2c/examples/matlab.mat differ diff --git a/THESE/codes/wave/allcode.zip b/THESE/codes/wave/allcode.zip new file mode 100644 index 0000000..91de146 Binary files /dev/null and b/THESE/codes/wave/allcode.zip differ diff --git a/THESE/codes/wave/allcode/.DS_Store b/THESE/codes/wave/allcode/.DS_Store new file mode 100644 index 0000000..a31c433 Binary files /dev/null and b/THESE/codes/wave/allcode/.DS_Store differ diff --git a/THESE/codes/wave/allcode/AntonB.m b/THESE/codes/wave/allcode/AntonB.m new file mode 100644 index 0000000..90fc164 --- /dev/null +++ b/THESE/codes/wave/allcode/AntonB.m @@ -0,0 +1,138 @@ +function [af, sf] = AntonB + + +a0 = [ + 0 + 0.02674875741081 + -0.01686411844287 + -0.07822326652899 + 0.26686411844288 + 0.60294901823636 + 0.26686411844287 + -0.07822326652899 + -0.01686411844287 + 0.02674875741081 + 0 + 0 +]; + +a1 = [ + 0 + 0 + 0 + 0.04563588155712 + -0.02877176311425 + -0.29563588155712 + 0.55754352622850 + -0.29563588155713 + -0.02877176311425 + 0.04563588155712 + 0 + 0 +]; + +s0 = [ + 0 + 0 + 0 + -0.04563588155712 + -0.02877176311425 + 0.29563588155712 + 0.55754352622850 + 0.29563588155713 + -0.02877176311425 + -0.04563588155712 + 0 + 0 +]; + +s1 = [ + 0 + 0.02674875741081 + 0.01686411844287 + -0.07822326652899 + -0.26686411844288 + 0.60294901823636 + -0.26686411844287 + -0.07822326652899 + 0.01686411844287 + 0.02674875741081 + 0 + 0 +]; + +s0 = 2*s0; +s1 = 2*s1; + +aa0 = [ + 0 + 0 + 0.02674875741081 + -0.01686411844287 + -0.07822326652899 + 0.26686411844288 + 0.60294901823636 + 0.26686411844287 + -0.07822326652899 + -0.01686411844287 + 0.02674875741081 + 0 +]; + +aa1 = [ + 0 + 0 + 0 + 0 + 0.04563588155712 + -0.02877176311425 + -0.29563588155712 + 0.55754352622850 + -0.29563588155713 + -0.02877176311425 + 0.04563588155712 + 0 + +]; + +ss0 = [ + 0 + 0 + -0.04563588155712 + -0.02877176311425 + 0.29563588155712 + 0.55754352622850 + 0.29563588155713 + -0.02877176311425 + -0.04563588155712 + 0 + 0 + 0 +]; + +ss1 = [ + 0.02674875741081 + 0.01686411844287 + -0.07822326652899 + -0.26686411844288 + 0.60294901823636 + -0.26686411844287 + -0.07822326652899 + 0.01686411844287 + 0.02674875741081 + 0 +0 +0 +]; + +ss0 = 2*ss0; +ss1 = 2*ss1; + + +af{1} = [a0 a1]; + +sf{1} = [s0 s1]; + +af{2} = [aa0 aa1]; + +sf{2} = [ss0 ss1]; \ No newline at end of file diff --git a/THESE/codes/wave/allcode/FSfarras.m b/THESE/codes/wave/allcode/FSfarras.m new file mode 100644 index 0000000..b9231ac --- /dev/null +++ b/THESE/codes/wave/allcode/FSfarras.m @@ -0,0 +1,44 @@ +function [af, sf] = FSfarras + +% Farras filters organized for the dual-tree +% complex DWT. +% +% USAGE: +% [af, sf] = FSfarras +% OUTPUT: +% af{i}, i = 1,2 - analysis filters for tree i +% sf{i}, i = 1,2 - synthesis filters for tree i +% See farras, dualtree, dualfilt1. +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +af{1} = [ + 0 0 + -0.08838834764832 -0.01122679215254 + 0.08838834764832 0.01122679215254 + 0.69587998903400 0.08838834764832 + 0.69587998903400 0.08838834764832 + 0.08838834764832 -0.69587998903400 + -0.08838834764832 0.69587998903400 + 0.01122679215254 -0.08838834764832 + 0.01122679215254 -0.08838834764832 + 0 0 + ]; + +sf{1} = af{1}(end:-1:1, :); + +af{2} = [ + 0.01122679215254 0 + 0.01122679215254 0 + -0.08838834764832 -0.08838834764832 + 0.08838834764832 -0.08838834764832 + 0.69587998903400 0.69587998903400 + 0.69587998903400 -0.69587998903400 + 0.08838834764832 0.08838834764832 + -0.08838834764832 0.08838834764832 + 0 0.01122679215254 + 0 -0.01122679215254 +]; + +sf{2} = af{2}(end:-1:1, :); diff --git a/THESE/codes/wave/allcode/afb.m b/THESE/codes/wave/allcode/afb.m new file mode 100644 index 0000000..2e03dd4 --- /dev/null +++ b/THESE/codes/wave/allcode/afb.m @@ -0,0 +1,41 @@ +function [lo, hi] = afb(x, af) + +% Analysis filter bank +% +% USAGE: +% [lo, hi] = afb(x, af) +% INPUT: +% x - N-point vector, where +% 1) N is even +% 2) N >= length(af) +% af - analysis filters +% af(:, 1) - lowpass filter (even length) +% af(:, 2) - highpass filter (even length) +% OUTPUT: +% lo - Low frequecy output +% hi - High frequency output +% EXAMPLE: +% [af, sf] = farras; +% x = rand(1,64); +% [lo, hi] = afb(x, af); +% y = sfb(lo, hi, sf); +% err = x - y; +% max(abs(err)) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +N = length(x); +L = length(af)/2; +x = cshift(x,-L); + +% lowpass filter +lo = upfirdn(x, af(:,1), 1, 2); +lo(1:L) = lo(N/2+[1:L]) + lo(1:L); +lo = lo(1:N/2); + +% highpass filter +hi = upfirdn(x, af(:,2), 1, 2); +hi(1:L) = hi(N/2+[1:L]) + hi(1:L); +hi = hi(1:N/2); + diff --git a/THESE/codes/wave/allcode/afb2D.m b/THESE/codes/wave/allcode/afb2D.m new file mode 100644 index 0000000..56265dd --- /dev/null +++ b/THESE/codes/wave/allcode/afb2D.m @@ -0,0 +1,40 @@ +function [lo, hi] = afb2D(x, af1, af2) + +% 2D Analysis Filter Bank +% +% USAGE: +% [lo, hi] = afb2D(x, af1, af2); +% INPUT: +% x - N by M matrix +% 1) M, N are both even +% 2) M >= 2*length(af1) +% 3) N >= 2*length(af2) +% af1 - analysis filters for columns +% af2 - analysis filters for rows +% OUTPUT: +% lo - lowpass subband +% hi{1} - 'lohi' subband +% hi{2} - 'hilo' subband +% hi{3} - 'hihi' subband +% EXAMPLE: +% x = rand(32,64); +% [af, sf] = farras; +% [lo, hi] = afb2D(x, af, af); +% y = sfb2D(lo, hi, sf, sf); +% err = x - y; +% max(max(abs(err))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +if nargin < 3 + af2 = af1; +end + +% filter along columns +[L, H] = afb2D_A(x, af1, 1); + +% filter along rows +[lo, hi{1}] = afb2D_A(L, af2, 2); +[hi{2}, hi{3}] = afb2D_A(H, af2, 2); + diff --git a/THESE/codes/wave/allcode/afb2D_A.m b/THESE/codes/wave/allcode/afb2D_A.m new file mode 100644 index 0000000..54cb26e --- /dev/null +++ b/THESE/codes/wave/allcode/afb2D_A.m @@ -0,0 +1,49 @@ +function [lo, hi] = afb2D_A(x, af, d) + +% 2D Analysis Filter Bank +% (along one dimension only) +% +% [lo, hi] = afb2D_A(x, af, d); +% INPUT: +% x - NxM matrix, where min(N,M) > 2*length(filter) +% (N, M are even) +% af - analysis filter for the columns +% af(:, 1) - lowpass filter +% af(:, 2) - highpass filter +% d - dimension of filtering (d = 1 or 2) +% OUTPUT: +% lo, hi - lowpass, highpass subbands +% +% % Example +% x = rand(32,64); +% [af, sf] = farras; +% [lo, hi] = afb2D_A(x, af, 1); +% y = sfb2D_A(lo, hi, sf, 1); +% err = x - y; +% max(max(abs(err))) + +lpf = af(:, 1); % lowpass filter +hpf = af(:, 2); % highpass filter + +if d == 2 + x = x'; +end + +N = size(x,1); +L = size(af,1)/2; +x = cshift2D(x,-L); + +lo = upfirdn(x, lpf, 1, 2); +lo(1:L, :) = lo(1:L, :) + lo([1:L]+N/2, :); +lo = lo(1:N/2, :); + +hi = upfirdn(x, hpf, 1, 2); +hi(1:L, :) = hi(1:L, :) + hi([1:L]+N/2, :); +hi = hi(1:N/2, :); + +if d == 2 + lo = lo'; + hi = hi'; +end + + diff --git a/THESE/codes/wave/allcode/afb3D.m b/THESE/codes/wave/allcode/afb3D.m new file mode 100644 index 0000000..44e56d0 --- /dev/null +++ b/THESE/codes/wave/allcode/afb3D.m @@ -0,0 +1,114 @@ +function [lo, hi] = afb3D(x, af1, af2, af3) + +% 3D Analysis Filter Bank +% +% USAGE: +% [lo, hi] = afb3D(x, af1, af2, af3); +% INPUT: +% x - N1 by N2 by N3 array matrix, where +% 1) N1, N2, N3 all even +% 2) N1 >= 2*length(af1) +% 3) N2 >= 2*length(af2) +% 4) N3 >= 2*length(af3) +% afi - analysis filters for dimension i +% afi(:, 1) - lowpass filter +% afi(:, 2) - highpass filter +% OUTPUT: +% lo - lowpass subband +% hi{d}, d = 1..7 - highpass subbands +% EXAMPLE: +% x = rand(32,64,16); +% [af, sf] = farras; +% [lo, hi] = afb3D(x, af, af, af); +% y = sfb3D(lo, hi, sf, sf, sf); +% err = x - y; +% max(max(max(abs(err)))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +if nargin < 3 + af2 = af1; + af3 = af1; +end + +% filter along dimension 1 +[L, H] = afb3D_A(x, af1, 1); + +% filter along dimension 2 +[LL LH] = afb3D_A(L, af2, 2); +[HL HH] = afb3D_A(H, af2, 2); + +% filter along dimension 3 +[LLL LLH] = afb3D_A(LL, af3, 3); +[LHL LHH] = afb3D_A(LH, af3, 3); +[HLL HLH] = afb3D_A(HL, af3, 3); +[HHL HHH] = afb3D_A(HH, af3, 3); + +lo = LLL; +hi{1} = LLH; +hi{2} = LHL; +hi{3} = LHH; +hi{4} = HLL; +hi{5} = HLH; +hi{6} = HHL; +hi{7} = HHH; + + +% LOCAL FUNCTION + +function [lo, hi] = afb3D_A(x, af, d) + +% 3D Analysis Filter Bank +% (along one dimension only) +% +% [lo, hi] = afb3D_A(x, af, d); +% INPUT: +% x - N1xN2xN2 matrix, where min(N1,N2,N3) > 2*length(filter) +% (Ni are even) +% af - analysis filter for the columns +% af(:, 1) - lowpass filter +% af(:, 2) - highpass filter +% d - dimension of filtering (d = 1, 2 or 3) +% OUTPUT: +% lo, hi - lowpass, highpass subbands +% +% % Example +% x = rand(32,64,16); +% [af, sf] = farras; +% d = 2; +% [lo, hi] = afb3D_A(x, af, d); +% y = sfb3D_A(lo, hi, sf, d); +% err = x - y; +% max(max(max(abs(err)))) + +lpf = af(:, 1); % lowpass filter +hpf = af(:, 2); % highpass filter + +% permute dimensions of x so that dimension d is first. +p = mod(d-1+[0:2], 3) + 1; +x = permute(x, p); + +% filter along dimension 1 +[N1, N2, N3] = size(x); +L = size(af, 1)/2; +x = cshift3D(x, -L, 1); +lo = zeros(L+N1/2, N2, N3); +hi = zeros(L+N1/2, N2, N3); + +for k = 1:N3 + lo(:, :, k) = upfirdn(x(:, :, k), lpf, 1, 2); +end +lo(1:L, :, :) = lo(1:L, :, :) + lo([1:L]+N1/2, :, :); +lo = lo(1:N1/2, :, :); + +for k = 1:N3 + hi(:, :, k) = upfirdn(x(:, :, k), hpf, 1, 2); +end +hi(1:L, :, :) = hi(1:L, :, :) + hi([1:L]+N1/2, :, :); +hi = hi(1:N1/2, :, :); + +% permute dimensions of x (inverse permutation) +lo = ipermute(lo, p); +hi = ipermute(hi, p); + diff --git a/THESE/codes/wave/allcode/barbara b/THESE/codes/wave/allcode/barbara new file mode 100644 index 0000000..47fae9c --- /dev/null +++ b/THESE/codes/wave/allcode/barbara @@ -0,0 +1 @@ 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$1\<1Y(GH(B##&,%"(377)-;806617?>HPBEQKNKRXXUNUQY^esigjnopr`_bcdfcaa_^`bd`adbaeea`cegc`_aeelhcjccc`Z:0<40435-04+0.*)%)+--+,''/-()##+092/0,-(.0283=@==97:B??A7<<:A58A7=JHDIKHEKJKNJKQOG<7DPVNISICGBIFCC402(#)'%%#%!#  & )1/36?@>D>@DCEDIB=CGQ\]bhdloqwquokoormppjnlimkfeghjhchopuqqokuonrlhejjonotmrprvwsnptqmjml_WG<7/%-Qaehilpqsqmqmqplpilrw{{|}y{{vuvy‚††”—šŸ¡¤¢¤¢ ¡œœœ›œžœš˜”‘†{scbT[Ž™¡¤£ž˜–‡|pcQMM\l}•¡¦£¤¢¡¤Ÿ›’“‚{vvlhcXNMG@A756.)#7A%SA3a3:I&"+$ $*21/22&+89>;213+0=9;DLIMTHELRXULXYRZam_Ybdqhm \ No newline at end of file diff --git a/THESE/codes/wave/allcode/bishrink.m b/THESE/codes/wave/allcode/bishrink.m new file mode 100644 index 0000000..9f7261f --- /dev/null +++ b/THESE/codes/wave/allcode/bishrink.m @@ -0,0 +1,15 @@ +function [w1] = bishrink(y1,y2,T) +% Bivariate Shrinkage Function +% Usage : +% [w1] = bishrink(y1,y2,T) +% INPUT : +% y1 - a noisy coefficient value +% y2 - the corresponding parent value +% T - threshold value +% OUTPUT : +% w1 - the denoised coefficient + +R = sqrt(abs(y1).^2 + abs(y2).^2); +R = R - T; +R = R .* (R > 0); +w1 = y1 .* R./(R+T); \ No newline at end of file diff --git a/THESE/codes/wave/allcode/boat b/THESE/codes/wave/allcode/boat new file mode 100644 index 0000000..09f55dd --- /dev/null +++ b/THESE/codes/wave/allcode/boat @@ -0,0 +1,321 @@ 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\ No newline at end of file diff --git a/THESE/codes/wave/allcode/cden2.m b/THESE/codes/wave/allcode/cden2.m new file mode 100644 index 0000000..d51cfdf --- /dev/null +++ b/THESE/codes/wave/allcode/cden2.m @@ -0,0 +1,15 @@ +function e = cden2(s,x,t) + +% % Example +% s1 = double(imread('st.tif')); +% s = s1(:,:,3);% x = s + 20*randn(size(s)); +% t = 0:5:45; +% e = cden2(s,x,t); +% plot(t,e); + +N = length(t); +for k = 1:N + y = denC2D(x,t(k)); + e(k) = sqrt(mean(mean((y-s).^2))); +end + diff --git a/THESE/codes/wave/allcode/cplxdual2D.m b/THESE/codes/wave/allcode/cplxdual2D.m new file mode 100644 index 0000000..2d980af --- /dev/null +++ b/THESE/codes/wave/allcode/cplxdual2D.m @@ -0,0 +1,51 @@ +function w = cplxdual2D(x, J, Faf, af) + +% Dual-Tree Complex 2D Discrete Wavelet Transform +% +% USAGE: +% w = cplxdual2D(x, J, Faf, af) +% INPUT: +% x - 2-D array +% J - number of stages +% Faf{i}: first stage filters for tree i +% af{i}: filters for remaining stages on tree i +% OUTPUT: +% w{j}{i}{d1}{d2} - wavelet coefficients +% j = 1..J (scale) +% i = 1 (real part); i = 2 (imag part) +% d1 = 1,2; d2 = 1,2,3 (orientations) +% w{J+1}{m}{n} - lowpass coefficients +% d1 = 1,2; d2 = 1,2 +% EXAMPLE: +% x = rand(256); +% J = 5; +% [Faf, Fsf] = FSfarras; +% [af, sf] = dualfilt1; +% w = cplxdual2D(x, J, Faf, af); +% y = icplxdual2D(w, J, Fsf, sf); +% err = x - y; +% max(max(abs(err))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% normalization +x = x/2; + +for m = 1:2 + for n = 1:2 + [lo w{1}{m}{n}] = afb2D(x, Faf{m}, Faf{n}); + for j = 2:J + [lo w{j}{m}{n}] = afb2D(lo, af{m}, af{n}); + end + w{J+1}{m}{n} = lo; + end +end + +for j = 1:J + for m = 1:3 + [w{j}{1}{1}{m} w{j}{2}{2}{m}] = pm(w{j}{1}{1}{m},w{j}{2}{2}{m}); + [w{j}{1}{2}{m} w{j}{2}{1}{m}] = pm(w{j}{1}{2}{m},w{j}{2}{1}{m}); + end +end + diff --git a/THESE/codes/wave/allcode/cplxdual2D_plots.m b/THESE/codes/wave/allcode/cplxdual2D_plots.m new file mode 100644 index 0000000..62e5ea3 --- /dev/null +++ b/THESE/codes/wave/allcode/cplxdual2D_plots.m @@ -0,0 +1,32 @@ +% cplxdual2D_plots +% DISPLAY 2D WAVELETS OF cplxdual2D.M + +J = 4; +L = 3*2^(J+1); +N = L/2^J; +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; +x = zeros(2*L,6*L); +w = cplxdual2D(x, J, Faf, af); +w{J}{1}{2}{2}(N/2,N/2+0*N) = 1; +w{J}{1}{1}{3}(N/2,N/2+1*N) = 1; +w{J}{1}{2}{1}(N/2,N/2+2*N) = 1; +w{J}{1}{1}{1}(N/2,N/2+3*N) = 1; +w{J}{1}{2}{3}(N/2,N/2+4*N) = 1; +w{J}{1}{1}{2}(N/2,N/2+5*N) = 1; +w{J}{2}{2}{2}(N/2+N,N/2+0*N) = 1; +w{J}{2}{1}{3}(N/2+N,N/2+1*N) = 1; +w{J}{2}{2}{1}(N/2+N,N/2+2*N) = 1; +w{J}{2}{1}{1}(N/2+N,N/2+3*N) = 1; +w{J}{2}{2}{3}(N/2+N,N/2+4*N) = 1; +w{J}{2}{1}{2}(N/2+N,N/2+5*N) = 1; +y = icplxdual2D(w, J, Fsf, sf); +y = [y; sqrt(y(1:L,:).^2+y(L+[1:L],:).^2)]; +figure(1) +clf +imagesc(y); +title('2D Dual-Tree Complex Wavelets') +axis image +axis off +colormap(gray(128)) +print -djpeg95 cplxdual2D_plots \ No newline at end of file diff --git a/THESE/codes/wave/allcode/cplxdual3D.m b/THESE/codes/wave/allcode/cplxdual3D.m new file mode 100644 index 0000000..9d7de10 --- /dev/null +++ b/THESE/codes/wave/allcode/cplxdual3D.m @@ -0,0 +1,55 @@ +function w = cplxdual3D(x, J, Faf, af) + +% 3D Complex Dual-Tree Discrete Wavelet Transform +% +% USAGE: +% w = cplxdual3D(x, J, Faf, af) +% INPUT: +% x - 3D array +% J - number of stages +% Faf - first stage filters +% af - filters for remaining stages +% OUPUT: +% w{j}{m}{n}{p}{d} - wavelet coefficients +% j = 1..J, m = 1..2, n = 1..2, p = 1..2, d = 1..7 +% w{J+1}{m}{n}{d} - lowpass coefficients +% m = 1..2, n = 1..2, p = 1..2, d = 1..7 +% EXAMPLE: +% x = rand(64,64,64); +% J = 3; +% [Faf, Fsf] = FSfarras; +% [af, sf] = dualfilt1; +% w = cplxdual3D(x, J, Faf, af); +% y = icplxdual3D(w, J, Fsf, sf); +% err = x - y; +% max(max(max(abs(err)))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% normalization +x = x/sqrt(8); + +for m = 1:2 + for n = 1:2 + for p = 1:2 + [lo w{1}{m}{n}{p}] = afb3D(x, Faf{m}, Faf{n}, Faf{p}); + for j = 2:J + [lo w{j}{m}{n}{p}] = afb3D(lo, af{m}, af{n}, af{p}); + end + w{J+1}{m}{n}{p} = lo; + end + end +end + +for j = 1:J + for m = 1:7 + [w{j}{1}{1}{1}{m} w{j}{2}{2}{1}{m} w{j}{2}{1}{2}{m} w{j}{1}{2}{2}{m}] = ... + pm4(w{j}{1}{1}{1}{m}, w{j}{2}{2}{1}{m}, w{j}{2}{1}{2}{m}, w{j}{1}{2}{2}{m}); + [w{j}{2}{2}{2}{m} w{j}{1}{1}{2}{m} w{j}{1}{2}{1}{m} w{j}{2}{1}{1}{m}] = ... + pm4(w{j}{2}{2}{2}{m}, w{j}{1}{1}{2}{m}, w{j}{1}{2}{1}{m}, w{j}{2}{1}{1}{m}); + end +end + + + diff --git a/THESE/codes/wave/allcode/cplxdual3D_plots.m b/THESE/codes/wave/allcode/cplxdual3D_plots.m new file mode 100644 index 0000000..92d36ac --- /dev/null +++ b/THESE/codes/wave/allcode/cplxdual3D_plots.m @@ -0,0 +1,90 @@ +% cplxdual3D_plots +% DISPLAY 3D WAVELETS OF CPLXDUAL3D.M + +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; +J = 4; +L = 2*2^(J+1); +N = L/2^J; +x = zeros(L,L,L); +w = cplxdual3D(x, J, Faf, af); +w{J}{1}{1}{1}{3}(N/2,N/2,N/2) = 1; +y = icplxdual3D(w, J, Fsf, sf); +figure(1) +clf +v = 1:L; +S = 0.0010; +p1 = patch(isosurface(v,v,v,y,S)); +isonormals(v,v,v,y,p1); +set(p1,'FaceColor','red','EdgeColor','none'); +hold on +p2 = patch(isosurface(v,v,v,y,-S)); +isonormals(v,v,v,y,p2); +set(p2,'FaceColor','blue','EdgeColor','none'); +hold off +daspect([1 1 1]); +view(-30,30); +camlight; +lighting phong +grid +axis([12 38 12 38 12 38]) +set(gca,'fontsize',7) +title('3-D WAVELET ISOSURFACE (REAL PART)') +set(gcf,'paperposition',[0.5 0.5 0 0]+[0 0 3 3]) +print -djpeg95 cplxdual3D_plots_1 +print -depsc cplxdual3D_plots_1 +yr = y; + +w{J}{1}{1}{1}{3}(N/2,N/2,N/2) = 0; +w{J}{2}{2}{2}{3}(N/2,N/2,N/2) = 1; +y = icplxdual3D(w, J, Fsf, sf); +figure(2) +clf +v = 1:L; +S = 0.0010; +p1 = patch(isosurface(v,v,v,y,S)); +isonormals(v,v,v,y,p1); +set(p1,'FaceColor','red','EdgeColor','none'); +hold on +p2 = patch(isosurface(v,v,v,y,-S)); +isonormals(v,v,v,y,p2); +set(p2,'FaceColor','blue','EdgeColor','none'); +hold off +daspect([1 1 1]); +view(-30,30); +camlight; +lighting phong +grid +axis([12 38 12 38 12 38]) +set(gca,'fontsize',7) +title('3-D WAVELET ISOSURFACE (IMAGINARY PART)') +set(gcf,'paperposition',[0.5 0.5 0 0]+[0 0 3 3]) +print -djpeg95 cplxdual3D_plots_2 +print -depsc cplxdual3D_plots_2 +yi = y; + +y = sqrt(yr.^2 + yi.^2); +figure(3) +clf +v = 1:L; +S = 0.0010; +p1 = patch(isosurface(v,v,v,y,S)); +isonormals(v,v,v,y,p1); +set(p1,'FaceColor','red','EdgeColor','none'); +hold on +p2 = patch(isosurface(v,v,v,y,-S)); +isonormals(v,v,v,y,p2); +set(p2,'FaceColor','blue','EdgeColor','none'); +hold off +daspect([1 1 1]); +view(-30,30); +camlight; +lighting phong +grid +axis([12 38 12 38 12 38]) +set(gca,'fontsize',7) +title('3-D WAVELET ISOSURFACE (MAGNITUDE)') +set(gcf,'paperposition',[0.5 0.5 0 0]+[0 0 3 3]) +print -djpeg95 cplxdual3D_plots_3 +print -depsc cplxdual3D_plots_3 + diff --git a/THESE/codes/wave/allcode/cshift.m b/THESE/codes/wave/allcode/cshift.m new file mode 100644 index 0000000..122d5b5 --- /dev/null +++ b/THESE/codes/wave/allcode/cshift.m @@ -0,0 +1,20 @@ +function y = cshift(x, m) + +% Circular Shift +% +% USAGE: +% y = cshift(x, m) +% INPUT: +% x - N-point vector +% m - amount of shift +% OUTPUT: +% y - vector x will be shifed by m samples to the left +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +N = length(x); +n = 0:N-1; +n = mod(n-m, N); +y = x(n+1); + diff --git a/THESE/codes/wave/allcode/cshift2D.m b/THESE/codes/wave/allcode/cshift2D.m new file mode 100644 index 0000000..1103da2 --- /dev/null +++ b/THESE/codes/wave/allcode/cshift2D.m @@ -0,0 +1,20 @@ +function y = cshift2D(x, m) + +% 2D Circular Shift +% +% USAGE: +% y = cshift2D(x, m) +% INPUT: +% x - M by N array +% m - amount of shift +% OUTPUT: +% y - matrix x will be shifed by m samples down +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +[N, M] = size(x); +n = 0:N-1; +n = mod(n-m, N); +y = x(n+1,:); + diff --git a/THESE/codes/wave/allcode/cshift3D.m b/THESE/codes/wave/allcode/cshift3D.m new file mode 100644 index 0000000..55d55bd --- /dev/null +++ b/THESE/codes/wave/allcode/cshift3D.m @@ -0,0 +1,33 @@ +function y = cshift3D(x, m, d) + +% 3D Circular Shift +% +% USAGE: +% y = cshift3D(x, m, d) +% INPUT: +% x - N1 by N2 by N3 array +% m - amount of shift +% d - dimension of shift (d = 1,2,3) +% OUTPUT: +% y - array x will be shifed by m samples down +% along dimension d +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +[N1, N2, N3] = size(x); +switch d +case 1 + n = 0:N1-1; + n = mod(n-m, N1); + y = x(n+1,:,:); +case 2 + n = 0:N2-1; + n = mod(n-m, N2); + y = x(:,n+1,:); +case 3 + n = 0:N3-1; + n = mod(n-m, N3); + y = x(:,:,n+1); +end + diff --git a/THESE/codes/wave/allcode/den2.m b/THESE/codes/wave/allcode/den2.m new file mode 100644 index 0000000..16cb1bc --- /dev/null +++ b/THESE/codes/wave/allcode/den2.m @@ -0,0 +1,15 @@ +function e = den2(s,x,t) + +% % Example +% s1 = double(imread('st.tif')); +% s = s1(:,:,3);% x = s + 20*randn(size(s)); +% t = 0:5:45; +% e = den2(s,x,t); +% plot(t,e); + +N = length(t); +for k = 1:N + y = denS2D(x,t(k)); + e(k) = sqrt(mean(mean((y-s).^2))); +end + diff --git a/THESE/codes/wave/allcode/den3.m b/THESE/codes/wave/allcode/den3.m new file mode 100644 index 0000000..3b54ee7 --- /dev/null +++ b/THESE/codes/wave/allcode/den3.m @@ -0,0 +1,64 @@ + + +s1 = double(imread('st.tif')); +s = s1(:,:,3); +x = s + 20*randn(size(s)); +t = 0:5:50; % threshold range(0~50),increment by 5 +e = den2(s,x,t); % using standard method +re = rden2(s,x,t); % using reduced 2-D dual-tree method +ce = cden2(s,x,t); % using complex 2-D dual-tree method + +figure(1) +plot(t,e) +hold on +plot(t,re,'r') +hold on +plot(t,ce,'k') +title('RMS error V.S. Threshold Pt.') +xlabel('Threshold pt.'); +ylabel('RMS error'); +legend('Standard 2D','Reduced 2D dual','Complx 2D dual',0); + + +[emin,k] = min(e); +T = t(k); +y = denS2D(x,T); +figure(2) +clf +image(y/4) +colormap(gray) +axis image +axis off +print -deps den3_A + +ax = [280*[1 1] 250*[1 1]]+[0 1 0 1]*120; + +figure(3) +clf +image(y/4) +colormap(gray) +axis(ax) +axis square +axis off +print -deps den3_B + +[emin,k] = min(ce); +T = t(k); +y = denC2D(x,T); +figure(4) +clf +image(y/4) +colormap(gray) +axis image +axis off +print -deps cden3_A + +figure(5) +clf +image(y/4) +colormap(gray) +axis(ax) +axis square +axis off +print -deps cden3_B + diff --git a/THESE/codes/wave/allcode/denC2D.m b/THESE/codes/wave/allcode/denC2D.m new file mode 100644 index 0000000..e10e831 --- /dev/null +++ b/THESE/codes/wave/allcode/denC2D.m @@ -0,0 +1,32 @@ +function y = denC2D(x,T); + +% % Example +% s1 = double(imread('st.tif')); +% s = s1(:,:,3); +% x = s + 20*randn(size(s)); +% T = 40; +% y = denC2D(x,T); +% imagesc(y) +% colormap(gray) +% axis image +% sqrt(mean(mean((y-s).^2))) + +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; +J = 4; +w = cplxdual2D(x,J,Faf,af); +I = sqrt(-1); +% loop thru scales: +for j = 1:J + % loop thru subbands + for s1 = 1:2 + for s2 = 1:3 + C = w{j}{1}{s1}{s2} + I*w{j}{2}{s1}{s2}; + C = soft(C,T); + w{j}{1}{s1}{s2} = real(C); + w{j}{2}{s1}{s2} = imag(C); + end + end +end +y = icplxdual2D(w,J,Fsf,sf); + diff --git a/THESE/codes/wave/allcode/denR2D.m b/THESE/codes/wave/allcode/denR2D.m new file mode 100644 index 0000000..baebfb4 --- /dev/null +++ b/THESE/codes/wave/allcode/denR2D.m @@ -0,0 +1,28 @@ +function y = denR2D(x,T); + +% % Example +% s1 = double(imread('st.tif')); +% s = s1(:,:,3); +% x = s + 20*randn(size(s)); +% T = 10; +% y = denR2D(x,T); +% imagesc(y) +% colormap(gray) +% axis image +% sqrt(mean(mean((y-s).^2))) + +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; +J = 4; +w = dualtree2D(x,J,Faf,af); +% loop thru scales: +for j = 1:J + % loop thru subbands + for s1 = 1:2 + for s2 = 1:3 + w{j}{s1}{s2} = soft(w{j}{s1}{s2},T); + end + end +end +y = idualtree2D(w,J,Fsf,sf); + diff --git a/THESE/codes/wave/allcode/denS2D.m b/THESE/codes/wave/allcode/denS2D.m new file mode 100644 index 0000000..b7af441 --- /dev/null +++ b/THESE/codes/wave/allcode/denS2D.m @@ -0,0 +1,24 @@ +function y = denS2D(x,T) + +% % Example +% s1 = double(imread('st.tif')); +% s = s1(:,:,3); +% x = s + 20*randn(size(s)); +% T = 35; +% y = denS2D(x,T); +% imagesc(y) +% colormap(gray) +% axis image + +[af, sf] = farras; +J = 4; +w = dwt2D(x,J,af); +% loop thru scales: +for j = 1:J + % loop thru subbands + for s = 1:3 + w{j}{s} = soft(w{j}{s},T); + end +end +y = idwt2D(w,J,sf); + diff --git a/THESE/codes/wave/allcode/denoising_dtdwt.m b/THESE/codes/wave/allcode/denoising_dtdwt.m new file mode 100644 index 0000000..73377df --- /dev/null +++ b/THESE/codes/wave/allcode/denoising_dtdwt.m @@ -0,0 +1,80 @@ +function y = denoising_dtdwt(x) +% Local Adaptive Image Denoising Algorithm +% Usage : +% y = denoising_dtdwt(x) +% INPUT : +% x - a noisy image +% OUTPUT : +% y - the corresponding denoised image + +% Set the windowsize and the corresponding filter +windowsize = 7; +windowfilt = ones(1,windowsize)/windowsize; + +% Number of Stages +J = 6; +I=sqrt(-1); + +% symmetric extension +L = length(x); % length of the original image. +N = L+2^J; % length after extension. +x = symextend(x,2^(J-1)); + +load nor_dualtree % run normaliz_coefcalc_dual_tree to generate this mat file. + +% Forward dual-tree DWT +% Either FSfarras or AntonB function can be used to compute the stage 1 filters +%[Faf, Fsf] = FSfarras; +[Faf, Fsf] = AntonB; +[af, sf] = dualfilt1; +W = cplxdual2D(x, J, Faf, af); +W = normcoef(W,J,nor); + + +% Noise variance estimation using robust median estimator.. +tmp = W{1}{1}{1}{1}; +Nsig = median(abs(tmp(:)))/0.6745; + +for scale = 1:J-1 + for dir = 1:2 + for dir1 = 1:3 + + % Noisy complex coefficients + %Real part + Y_coef_real = W{scale}{1}{dir}{dir1}; + % imaginary part + Y_coef_imag = W{scale}{2}{dir}{dir1}; + % The corresponding noisy parent coefficients + %Real part + Y_parent_real = W{scale+1}{1}{dir}{dir1}; + % imaginary part + Y_parent_imag = W{scale+1}{2}{dir}{dir1}; + % Extend noisy parent matrix to make the matrix size the same as the coefficient matrix. + Y_parent_real = expand(Y_parent_real); + Y_parent_imag = expand(Y_parent_imag); + + % Signal variance estimation + Wsig = conv2(windowfilt,windowfilt,(Y_coef_real).^2,'same'); + Ssig = sqrt(max(Wsig-Nsig.^2,eps)); + + % Threshold value estimation + T = sqrt(3)*Nsig^2./Ssig; + + % Bivariate Shrinkage + Y_coef = Y_coef_real+I*Y_coef_imag; + Y_parent = Y_parent_real + I*Y_parent_imag; + Y_coef = bishrink(Y_coef,Y_parent,T); + W{scale}{1}{dir}{dir1} = real(Y_coef); + W{scale}{2}{dir}{dir1} = imag(Y_coef); + + end + end +end + +% Inverse Transform +W = unnormcoef(W,J,nor); +y = icplxdual2D(W, J, Fsf, sf); + +% Extract the image +ind = 2^(J-1)+1:2^(J-1)+L; +y = y(ind,ind); \ No newline at end of file diff --git a/THESE/codes/wave/allcode/denoising_dwt.m b/THESE/codes/wave/allcode/denoising_dwt.m new file mode 100644 index 0000000..c73119f --- /dev/null +++ b/THESE/codes/wave/allcode/denoising_dwt.m @@ -0,0 +1,61 @@ +function y = denoising_dwt(x) +% Local Adaptive Image Denoising Algorithm +% Usage : +% y = denoising_dwt(x) +% INPUT : +% x - a noisy image +% OUTPUT : +% y - the corresponding denoised image + +% Adjust windowsize and the corresponding filter +windowsize = 7; +windowfilt = ones(1,windowsize)/windowsize; + +% Number of Stages +L = 6; + +% symmetric extension +N = length(x); +N = N+2^L; +x = symextend(x,2^(L-1)); + +% forward transform +[af, sf] = farras; +W = dwt2D(x,L,af); + +% Noise variance estimation using robust median estimator.. +tmp = W{1}{3}; +Nsig = median(abs(tmp(:)))/0.6745; + +for scale = 1:L-1 + for dir = 1:3 + + % noisy coefficients + Y_coefficient = W{scale}{dir}; + + % noisy parent + Y_parent = W{scale+1}{dir}; + + % extent Y_parent to make the matrix size be equal to Y_coefficient + Y_parent = expand(Y_parent); + + % Signal variance estimation + + Wsig = conv2(windowfilt,windowfilt,(Y_coefficient).^2,'same'); + Ssig = sqrt(max(Wsig-Nsig.^2,eps)); + + % Threshold value estimation + T = sqrt(3)*Nsig^2./Ssig; + + % Bivariate Shrinkage + W{scale}{dir} = bishrink(Y_coefficient,Y_parent,T); + + end +end + + +% Inverse Transform +y = idwt2D(W,L,sf); + +% Extract the image +y = y(2^(L-1)+1:2^(L-1)+512,2^(L-1)+1:2^(L-1)+512); \ No newline at end of file diff --git a/THESE/codes/wave/allcode/dualfilt1.m b/THESE/codes/wave/allcode/dualfilt1.m new file mode 100644 index 0000000..89e41ca --- /dev/null +++ b/THESE/codes/wave/allcode/dualfilt1.m @@ -0,0 +1,51 @@ +function [af, sf] = dualfilt1 + +% Kingsbury Q-filters for the dual-tree complex DWT +% +% USAGE: +% [af, sf] = dualfilt1 +% OUTPUT: +% af{i}, i = 1,2 - analysis filters for tree i +% sf{i}, i = 1,2 - synthesis filters for tree i +% note: af{2} is the reverse of af{1} +% REFERENCE: +% N. G. Kingsbury, "A dual-tree complex wavelet +% transform with improved orthogonality and symmetry +% properties", Proceedings of the IEEE Int. Conf. on +% Image Proc. (ICIP), 2000 +% See dualtree +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% These cofficients are rounded to 8 decimal places. + +af{1} = [ + 0.03516384000000 0 + 0 0 + -0.08832942000000 -0.11430184000000 + 0.23389032000000 0 + 0.76027237000000 0.58751830000000 + 0.58751830000000 -0.76027237000000 + 0 0.23389032000000 + -0.11430184000000 0.08832942000000 + 0 0 + 0 -0.03516384000000 + ]; + +af{2} = [ + 0 -0.03516384000000 + 0 0 + -0.11430184000000 0.08832942000000 + 0 0.23389032000000 + 0.58751830000000 -0.76027237000000 + 0.76027237000000 0.58751830000000 + 0.23389032000000 0 + -0.08832942000000 -0.11430184000000 + 0 0 + 0.03516384000000 0 +]; + +sf{1} = af{1}(end:-1:1, :); + +sf{2} = af{2}(end:-1:1, :); diff --git a/THESE/codes/wave/allcode/dualtree.m b/THESE/codes/wave/allcode/dualtree.m new file mode 100644 index 0000000..8a32759 --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree.m @@ -0,0 +1,49 @@ +function w = dualtree(x, J, Faf, af) + +% Dual-tree Complex Discrete Wavelet Transform +% +% USAGE: +% w = dualtree(x, J, Faf, af) +% INPUT: +% x - N-point vector +% 1) N is divisible by 2^J +% 2) N >= 2^(J-1)*length(af) +% J - number of stages +% Faf - filters for the first stage +% af - filters for the remaining stages +% OUTPUT: +% w - DWT coefficients +% w{j}{1}, j = 1..J - real part +% w{j}{2}, j = 1..J - imaginary part +% w{J+1}{d} - lowpass coefficients, d = 1,2 +% EXAMPLE: +% x = rand(1, 512); +% J = 4; +% [Faf, Fsf] = FSfarras; +% [af, sf] = dualfilt1; +% w = dualtree(x, J, Faf, af); +% y = idualtree(w, J, Fsf, sf); +% err = x - y; +% max(abs(err)) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% normalization +x = x/sqrt(2); + +% Tree 1 +[x1 w{1}{1}] = afb(x, Faf{1}); +for j = 2:J + [x1 w{j}{1}] = afb(x1, af{1}); +end +w{J+1}{1} = x1; + +% Tree 2 +[x2 w{1}{2}] = afb(x, Faf{2}); +for j = 2:J + [x2 w{j}{2}] = afb(x2, af{2}); +end +w{J+1}{2} = x2; + + diff --git a/THESE/codes/wave/allcode/dualtree2D.m b/THESE/codes/wave/allcode/dualtree2D.m new file mode 100644 index 0000000..730c078 --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree2D.m @@ -0,0 +1,56 @@ +function w = dualtree2D(x, J, Faf, af) + +% 2D Dual-Tree Discrete Wavelet Transform +% +% USAGE: +% w = dualtree2D(x, J, Faf, af) +% INPUT: +% x - M by N array +% J - number of stages +% Faf - first stage filters +% af - filters for remaining stages +% OUPUT: +% w{j}{d1}{d2} - DWT coefficients +% j = 1..J, k = 1..2, d = 1..3 +% w{J+1}{k} - lowpass coefficients +% k = 1..2 +% % EXAMPLE: +% x = rand(256); +% J = 3; +% [Faf, Fsf] = FSfarras; +% [af, sf] = dualfilt1; +% w = dualtree2D(x, J, Faf, af); +% y = idualtree2D(w, J, Fsf, sf); +% err = x - y; +% max(max(abs(err))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% normalization +x = x/sqrt(2); + +% Tree 1 +[x1 w{1}{1}] = afb2D(x, Faf{1}); % stage 1 +for j = 2:J + [x1 w{j}{1}] = afb2D(x1, af{1}); % remaining stages +end +w{J+1}{1} = x1; % lowpass subband + +% Tree 2 +[x2 w{1}{2}] = afb2D(x, Faf{2}); % stage 1 +for j = 2:J + [x2 w{j}{2}] = afb2D(x2, af{2}); % remaining stages +end +w{J+1}{2} = x2; % lowpass subband + +% sum and difference +for j = 1:J + for m = 1:3 + A = w{j}{1}{m}; + B = w{j}{2}{m}; + w{j}{1}{m} = (A+B)/sqrt(2); + w{j}{2}{m} = (A-B)/sqrt(2); + end +end + diff --git a/THESE/codes/wave/allcode/dualtree2D_plots.m b/THESE/codes/wave/allcode/dualtree2D_plots.m new file mode 100644 index 0000000..59e64b6 --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree2D_plots.m @@ -0,0 +1,26 @@ + +% dualtree2D_plots +% DISPLAY 2D WAVELETS OF dualtree2D.M + +J = 4; +L = 3*2^(J+1); +N = L/2^J; +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; +x = zeros(2*L,3*L); +w = dualtree2D(x, J, Faf, af); +w{J}{1}{1}(N/2,N/2+0*N) = 1; +w{J}{1}{2}(N/2,N/2+1*N) = 1; +w{J}{1}{3}(N/2,N/2+2*N) = 1; +w{J}{2}{1}(N/2+N,N/2+0*N) = 1; +w{J}{2}{2}(N/2+N,N/2+1*N) = 1; +w{J}{2}{3}(N/2+N,N/2+2*N) = 1; +y = idualtree2D(w, J, Fsf, sf); + +figure(1) +clf +imagesc(y); +axis equal +axis off +colormap(gray(128)) + diff --git a/THESE/codes/wave/allcode/dualtree3D.m b/THESE/codes/wave/allcode/dualtree3D.m new file mode 100644 index 0000000..8ae6830 --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree3D.m @@ -0,0 +1,59 @@ +function w = dualtree3D(x, J, Faf, af) + +% 3D Dual-Tree Discrete Wavelet Transform +% +% USAGE: +% w = dualtree3D(x, J, Faf, af) +% INPUT: +% x - 3-D array +% J - number of stages +% Faf - first stage filters +% af - filters for remaining stages +% OUPUT: +% w{j}{i}{d} - wavelet coefficients +% j = 1..J, i = 1..4, d = 1..7 +% w{J+1}{i} - lowpass coefficients +% i = 1..4 +% EXAMPLE: +% x = rand(64,64,64); +% J = 3; +% [Faf, Fsf] = FSfarras; +% [af, sf] = dualfilt1; +% w = dualtree3D(x, J, Faf, af); +% y = idualtree3D(w, J, Fsf, sf); +% err = x - y; +% max(max(max(abs(err)))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + + +% normalization +x = x/2; + +M = [ + 1 1 1 + 2 2 1 + 2 1 2 + 1 2 2 +]; + +for i = 1:4 + f1 = M(i,1); + f2 = M(i,2); + f3 = M(i,3); + [xi w{1}{i}] = afb3D(x, Faf{f1}, Faf{f2}, Faf{f3}); + for k = 2:J + [xi w{k}{i}] = afb3D(xi, af{f1}, af{f2}, af{f3}); + end + w{J+1}{i} = xi; +end + +for k = 1:J + for m = 1:7 + [w{k}{1}{m} w{k}{2}{m} w{k}{3}{m} w{k}{4}{m}] = ... + pm4(w{k}{1}{m}, w{k}{2}{m}, w{k}{3}{m}, w{k}{4}{m}); + end +end + + diff --git a/THESE/codes/wave/allcode/dualtree3D_plots.m b/THESE/codes/wave/allcode/dualtree3D_plots.m new file mode 100644 index 0000000..4d1cf5d --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree3D_plots.m @@ -0,0 +1,35 @@ +% dualtree3D_plots +% DISPLAY 3D WAVELETS OF DUALTREE3D.M + + +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; +J = 4; +L = 3*2^(J+1); +N = L/2^J; +x = zeros(L,L,L); +w = dualtree3D(x, J, Faf, af); +w{J}{4}{7}(N/2,N/2,N/2) = 1; +y = idualtree3D(w, J, Fsf, sf); +figure(1) +clf +v = 1:L; +S = 0.0025; +p1 = patch(isosurface(v,v,v,y,S)); +isonormals(v,v,v,y,p1); +set(p1,'FaceColor','red','EdgeColor','none'); +hold on +p2 = patch(isosurface(v,v,v,y,-S)); +isonormals(v,v,v,y,p2); +set(p2,'FaceColor','blue','EdgeColor','none'); +hold off +daspect([1 1 1]); +view(-30,30); +camlight; +lighting phong +grid +axis([32 48 32 48 32 48]) +set(gca,'fontsize',7) +title('3-D WAVELET ISOSURFACE (COMPLEX DT TRANSFORM)') +set(gcf,'paperposition',[0.5 0.5 0 0]+[0 0 4 3]) +print -djpeg95 dualtree3D_plots \ No newline at end of file diff --git a/THESE/codes/wave/allcode/dualtree_eg1.m b/THESE/codes/wave/allcode/dualtree_eg1.m new file mode 100644 index 0000000..5914f45 --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree_eg1.m @@ -0,0 +1,28 @@ +J = 5; % J: number of stages + +% get filters +[Faf, Fsf] = FSfarras; +[af, sf] = dualfilt1; + +x = zeros(1,256); % zero signal + +% Compute dual-tree complex DWT of zero signal +w = dualtree(x, J, Faf, af); +% Set a single (real) coefficient to 1 +w{5}{1}(4) = 1; +% Compute the inverse transform +y1 = idualtree(w, J, Fsf, sf); + +% Compute dual-tree complex DWT of zero signal +w = dualtree(x, J, Faf, af); +% Set a single (imaginary) coefficient to 1 +w{5}{2}(4) = 1; +% Compute the inverse transform +y2 = idualtree(w, J, Fsf, sf); + +% Display real and imaginary parts and magnitude +n = [1:256]/256; +plot(n,y1,n,y2,n,sqrt(y1.^2+y2.^2)) +title('COMPLEX 1D WAVELET') +xlabel('t'); +ylabel('\psi(t)'); diff --git a/THESE/codes/wave/allcode/dualtree_eg1_makeFigure.m b/THESE/codes/wave/allcode/dualtree_eg1_makeFigure.m new file mode 100644 index 0000000..e5c8e49 --- /dev/null +++ b/THESE/codes/wave/allcode/dualtree_eg1_makeFigure.m @@ -0,0 +1,41 @@ + +clear +close all + + +% Set defaults +font_size = 11; +set(0, 'DefaultAxesFontSize',font_size) + +line_width = 1; +set(0, 'DefaultLineLineWidth',line_width) + +font_size = 15; +set(0, 'DefaultTextFontSize',font_size) + + + + +subplot(2,1,1) + +dualtree_eg1 + +box off + +% remove defaults +set(0, 'DefaultAxesFontSize','remove') +set(0, 'DefaultLineLineWidth','remove') +set(0, 'DefaultTextFontSize','remove') + +if 1 + % paperposition + paperpos = [2 2 7 5]; % [left, bottom, width, height] + + set(gcf, 'PaperPositionMode', 'manual'); + set(gcf, 'PaperUnits', 'inches'); + set(gcf, 'PaperPosition', paperpos); +end + +print -depsc figures/dualtree_eg1_figure + +% then open eps file and save as png file. diff --git a/THESE/codes/wave/allcode/dwt.m b/THESE/codes/wave/allcode/dwt.m new file mode 100644 index 0000000..98a498e --- /dev/null +++ b/THESE/codes/wave/allcode/dwt.m @@ -0,0 +1,32 @@ +function w = dwt(x, J, af) + +% Discrete 1-D Wavelet Transform +% +% USAGE: +% w = dwt(x, J, af) +% INPUT: +% x - N-point vector, where +% 1) N is divisible by 2^J +% 2) N >= 2^(J-1)*length(af) +% J - number of stages +% af - analysis filters +% af(:, 1) - lowpass filter (even length) +% af(:, 2) - highpass filter (evenlength) +% OUTPUT: +% w{j}, j = 1..J+1 - DWT coefficients +% EXAMPLE: +% [af, sf] = farras; +% x = rand(1,64); +% w = dwt(x,3,af); +% y = idwt(w,3,sf); +% err = x - y; +% max(abs(err)) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +for k = 1:J + [x w{k}] = afb(x, af); +end +w{J+1} = x; + diff --git a/THESE/codes/wave/allcode/dwt2D.m b/THESE/codes/wave/allcode/dwt2D.m new file mode 100644 index 0000000..9c712d2 --- /dev/null +++ b/THESE/codes/wave/allcode/dwt2D.m @@ -0,0 +1,31 @@ +function w = dwt2D(x, J, af) + +% discrete 2-D wavelet transform +% +% USAGE: +% w = dwt2D(x, stages, af) +% INPUT: +% x - N by M matrix +% 1) M, N are both even +% 2) min(M,N) >= 2^(J-1)*length(af) +% J - number of stages +% af - analysis filters +% OUPUT: +% w - cell array of wavelet coefficients +% EXAMPLE: +% [af, sf] = farras; +% x = rand(128,64); +% J = 3; +% w = dwt2D(x,J,af); +% y = idwt2D(w,J,sf); +% err = x - y; +% max(max(abs(err))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +for k = 1:J + [x w{k}] = afb2D(x, af, af); +end +w{J+1} = x; + diff --git a/THESE/codes/wave/allcode/dwt2D_plots.m b/THESE/codes/wave/allcode/dwt2D_plots.m new file mode 100644 index 0000000..88f1023 --- /dev/null +++ b/THESE/codes/wave/allcode/dwt2D_plots.m @@ -0,0 +1,21 @@ +% dwt2D_plots +% DISPLAY 2D WAVELETS OF DWT2D.M + +[af, sf] = farras; +J = 5; +L = 3*2^(J+1); +N = L/2^J; +x = zeros(L,3*L); +w = dwt2D(x,J,af); +w{J}{1}(N/2,N/2+0*N) = 1; +w{J}{2}(N/2,N/2+1*N) = 1; +w{J}{3}(N/2,N/2+2*N) = 1; +y = idwt2D(w,J,sf); + +figure(1) +clf +imagesc(y); +axis equal +axis off +colormap(gray(128)) + diff --git a/THESE/codes/wave/allcode/dwt3D.m b/THESE/codes/wave/allcode/dwt3D.m new file mode 100644 index 0000000..e9faac4 --- /dev/null +++ b/THESE/codes/wave/allcode/dwt3D.m @@ -0,0 +1,30 @@ +function w = dwt3D(x, J, af) + +% 3-D Discrete Wavelet Transform +% +% USAGE: +% w = dwt3D(x, stages, af) +% INPUT: +% x - N1 by N2 by N3 matrix +% 1) Ni all even +% 2) min(Ni) >= 2^(J-1)*length(af) +% J - number of stages +% af - analysis filters +% OUTPUT: +% w - cell array of wavelet coefficients +% EXAMPLE: +% [af, sf] = farras; +% x = rand(128,64,64); +% w = dwt3D(x,3,af); +% y = idwt3D(w,3,sf); +% err = x-y; +% max(max(max(abs(err)))) +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +for k = 1:J + [x w{k}] = afb3D(x, af, af, af); +end +w{J+1} = x; + diff --git a/THESE/codes/wave/allcode/dwt3D_plots.m b/THESE/codes/wave/allcode/dwt3D_plots.m new file mode 100644 index 0000000..fa56e7d --- /dev/null +++ b/THESE/codes/wave/allcode/dwt3D_plots.m @@ -0,0 +1,33 @@ +% dwt3D_plots +% DISPLAY 3D WAVELETS OF DWT3D.M + +[af, sf] = farras; +J = 4; +L = 3*2^(J+1); +N = L/2^J; +x = zeros(L,L,L); +w = dwt3D(x,J,af); +w{J}{7}(N/2,N/2,N/2) = 1; +y = idwt3D(w,J,sf); +figure(1) +clf +v = 1:L; +S = 0.017; +p1 = patch(isosurface(v,v,v,y,S)); +isonormals(v,v,v,y,p1); +set(p1,'FaceColor','red','EdgeColor','none'); +hold on +p2 = patch(isosurface(v,v,v,y,-S)); +isonormals(v,v,v,y,p2); +set(p2,'FaceColor','blue','EdgeColor','none'); +hold off +daspect([1 1 1]); +view(-30,30); +camlight; +lighting phong +grid +axis([32 48 32 48 32 48]) +set(gca,'fontsize',7) +title('3-D WAVELET ISOSURFACE (SEPARABLE TRANSFORM)') +set(gcf,'paperposition',[0.5 0.5 0 0]+[0 0 4 3]) +print -djpeg95 dwt3D_plots diff --git a/THESE/codes/wave/allcode/expand.m b/THESE/codes/wave/allcode/expand.m new file mode 100644 index 0000000..b0297f5 --- /dev/null +++ b/THESE/codes/wave/allcode/expand.m @@ -0,0 +1,11 @@ +function[y] = expand(x) + +[N,M] = size(x); +N = N*2; +M = M*2; + +y = zeros(N,M); +y(1:2:N,1:2:M) = x; +y(2:2:N,2:2:M) = x; +y(1:2:N,2:2:M) = x; +y(2:2:N,1:2:M) = x; \ No newline at end of file diff --git a/THESE/codes/wave/allcode/farras.m b/THESE/codes/wave/allcode/farras.m new file mode 100644 index 0000000..cd6d1b5 --- /dev/null +++ b/THESE/codes/wave/allcode/farras.m @@ -0,0 +1,35 @@ +function [af, sf] = farras + +% Farras nearly symmetric filters for orthogonal +% 2-channel perfect reconstruction filter bank +% +% USAGE: +% [af, sf] = farras +% OUTPUT: +% af - analysis filters +% sf - synthesis filters +% REFERENCE: +% A. F. Abdelnour and I. W. Selesnick. +% "Nearly symmetric orthogonal wavelet bases", +% Proc. IEEE Int. Conf. Acoust., Speech, +% Signal Processing (ICASSP), May 2001. +% See afb, dwt. +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +af = [ + 0 -0.01122679215254 + 0 0.01122679215254 + -0.08838834764832 0.08838834764832 + 0.08838834764832 0.08838834764832 + 0.69587998903400 -0.69587998903400 + 0.69587998903400 0.69587998903400 + 0.08838834764832 -0.08838834764832 + -0.08838834764832 -0.08838834764832 + 0.01122679215254 0 + 0.01122679215254 0 +]; + +sf = af(end:-1:1, :); + diff --git a/THESE/codes/wave/allcode/figures/.DS_Store b/THESE/codes/wave/allcode/figures/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/THESE/codes/wave/allcode/figures/.DS_Store differ diff --git a/THESE/codes/wave/allcode/figures/dualtree_eg1_figure.eps b/THESE/codes/wave/allcode/figures/dualtree_eg1_figure.eps new file mode 100644 index 0000000..85decbb --- /dev/null +++ b/THESE/codes/wave/allcode/figures/dualtree_eg1_figure.eps @@ -0,0 +1,392 @@ +%!PS-Adobe-2.0 EPSF-1.2 +%%Creator: MATLAB, The Mathworks, Inc. +%%Title: figures/dualtree_eg1_figure.eps +%%CreationDate: 02/09/2010 10:33:03 +%%DocumentNeededFonts: Helvetica +%%DocumentProcessColors: Cyan Magenta Yellow Black +%%Extensions: CMYK +%%Pages: 1 +%%BoundingBox: 164 324 607 501 +%%EndComments + +%%BeginProlog +% MathWorks dictionary +/MathWorks 160 dict begin +% definition operators +/bdef {bind def} bind def +/ldef {load def} bind def +/xdef {exch def} bdef +/xstore {exch store} bdef +% operator abbreviations +/c /clip ldef +/cc /concat ldef +/cp /closepath ldef +/gr /grestore ldef +/gs /gsave ldef +/mt /moveto ldef +/np /newpath ldef +/cm /currentmatrix ldef +/sm /setmatrix ldef +/rm /rmoveto ldef +/rl /rlineto ldef +/s {show newpath} bdef +/sc {setcmykcolor} bdef +/sr /setrgbcolor ldef +/sg /setgray ldef +/w /setlinewidth ldef +/j /setlinejoin ldef +/cap /setlinecap ldef +/rc {rectclip} bdef +/rf {rectfill} bdef +% page state control +/pgsv () def +/bpage {/pgsv save def} bdef +/epage {pgsv restore} bdef +/bplot /gsave ldef +/eplot {stroke grestore} bdef +% orientation switch +/portraitMode 0 def /landscapeMode 1 def /rotateMode 2 def +% coordinate system mappings +/dpi2point 0 def +% font control +/FontSize 0 def +/FMS {/FontSize xstore findfont [FontSize 0 0 FontSize neg 0 0] + makefont setfont} bdef +/reencode {exch dup where {pop load} {pop StandardEncoding} ifelse + exch dup 3 1 roll findfont dup length dict begin + { 1 index /FID ne {def}{pop pop} ifelse } forall + /Encoding exch def currentdict end definefont pop} bdef +/isroman {findfont /CharStrings get /Agrave known} bdef +/FMSR {3 1 roll 1 index dup isroman {reencode} {pop pop} ifelse + exch FMS} bdef +/csm {1 dpi2point div -1 dpi2point div scale neg translate + dup landscapeMode eq {pop -90 rotate} + {rotateMode eq {90 rotate} if} ifelse} bdef +% line types: solid, dotted, dashed, dotdash +/SO { [] 0 setdash } bdef +/DO { [.5 dpi2point mul 4 dpi2point mul] 0 setdash } bdef +/DA { [6 dpi2point mul] 0 setdash } bdef +/DD { [.5 dpi2point mul 4 dpi2point mul 6 dpi2point mul 4 + dpi2point mul] 0 setdash } bdef +% macros for lines and objects +/L {lineto stroke} bdef +/MP {3 1 roll moveto 1 sub {rlineto} repeat} bdef +/AP {{rlineto} repeat} bdef +/PDlw -1 def +/W {/PDlw currentlinewidth def setlinewidth} def +/PP {closepath eofill} bdef +/DP {closepath stroke} bdef +/MR {4 -2 roll moveto dup 0 exch rlineto exch 0 rlineto + neg 0 exch rlineto closepath} bdef +/FR {MR stroke} bdef +/PR {MR fill} bdef +/L1i {{currentfile picstr readhexstring pop} image} bdef +/tMatrix matrix def +/MakeOval {newpath tMatrix currentmatrix pop translate scale +0 0 1 0 360 arc tMatrix setmatrix} bdef +/FO {MakeOval stroke} bdef +/PO {MakeOval fill} bdef +/PD {currentlinewidth 2 div 0 360 arc fill + PDlw -1 eq not {PDlw w /PDlw -1 def} if} def +/FA {newpath tMatrix currentmatrix pop translate scale + 0 0 1 5 -2 roll arc tMatrix setmatrix stroke} bdef +/PA {newpath tMatrix currentmatrix pop translate 0 0 moveto scale + 0 0 1 5 -2 roll arc closepath tMatrix setmatrix fill} bdef +/FAn {newpath tMatrix currentmatrix pop translate scale + 0 0 1 5 -2 roll arcn tMatrix setmatrix stroke} bdef +/PAn {newpath tMatrix currentmatrix pop translate 0 0 moveto scale + 0 0 1 5 -2 roll arcn closepath tMatrix setmatrix fill} bdef +/vradius 0 def /hradius 0 def /lry 0 def +/lrx 0 def /uly 0 def /ulx 0 def /rad 0 def +/MRR {/vradius xdef /hradius xdef /lry xdef /lrx xdef /uly xdef + /ulx xdef newpath tMatrix currentmatrix pop ulx hradius add uly + vradius add translate hradius vradius scale 0 0 1 180 270 arc + tMatrix setmatrix lrx hradius sub uly vradius add translate + hradius vradius scale 0 0 1 270 360 arc tMatrix setmatrix + lrx hradius sub lry vradius sub translate hradius vradius scale + 0 0 1 0 90 arc tMatrix setmatrix ulx hradius add lry vradius sub + translate hradius vradius scale 0 0 1 90 180 arc tMatrix setmatrix + closepath} bdef +/FRR {MRR stroke } bdef +/PRR {MRR fill } bdef +/MlrRR {/lry xdef /lrx xdef /uly xdef /ulx xdef /rad lry uly sub 2 div def + newpath tMatrix currentmatrix pop ulx rad add uly rad add translate + rad rad scale 0 0 1 90 270 arc tMatrix setmatrix lrx rad sub lry rad + sub translate rad rad scale 0 0 1 270 90 arc tMatrix setmatrix + closepath} bdef +/FlrRR {MlrRR stroke } bdef +/PlrRR {MlrRR fill } bdef +/MtbRR {/lry xdef /lrx xdef /uly xdef /ulx xdef /rad lrx ulx sub 2 div def + newpath tMatrix currentmatrix pop ulx rad add uly rad add translate + rad rad scale 0 0 1 180 360 arc tMatrix setmatrix lrx rad sub lry rad + sub translate rad rad scale 0 0 1 0 180 arc tMatrix setmatrix + closepath} bdef +/FtbRR {MtbRR stroke } bdef +/PtbRR {MtbRR fill } bdef +/stri 6 array def /dtri 6 array def +/smat 6 array def /dmat 6 array def +/tmat1 6 array def /tmat2 6 array def /dif 3 array def +/asub {/ind2 exch def /ind1 exch def dup dup + ind1 get exch ind2 get sub exch } bdef +/tri_to_matrix { + 2 0 asub 3 1 asub 4 0 asub 5 1 asub + dup 0 get exch 1 get 7 -1 roll astore } bdef +/compute_transform { + dmat dtri tri_to_matrix tmat1 invertmatrix + smat stri tri_to_matrix tmat2 concatmatrix } bdef +/ds {stri astore pop} bdef +/dt {dtri astore pop} bdef +/db {2 copy /cols xdef /rows xdef mul dup 3 mul string + currentfile exch readhexstring pop + dup 0 3 index getinterval /rbmap xdef + dup 2 index dup getinterval /gbmap xdef + 1 index dup 2 mul exch getinterval /bbmap xdef pop pop}bdef +/it {gs np dtri aload pop moveto lineto lineto cp c + cols rows 8 compute_transform + rbmap gbmap bbmap true 3 colorimage gr}bdef +/il {newpath moveto lineto stroke}bdef +currentdict end def +%%EndProlog + +%%BeginSetup +MathWorks begin + +0 cap + +end +%%EndSetup + +%%Page: 1 1 +%%BeginPageSetup +%%PageBoundingBox: 164 324 607 501 +MathWorks begin +bpage +%%EndPageSetup + +%%BeginObject: obj1 +bplot + +/dpi2point 12 def +portraitMode 1728 6048 csm + + 241 25 5325 2135 MR c np +125 dict begin %Colortable dictionary +/c0 { 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19 0 18 0 18 -1 19 -1 +18 -1 18 0 19 0 18 0 18 0 19 0 18 0 18 0 +19 0 18 0 18 0 19 0 18 0 18 0 19 0 18 0 +18 -1 805 1061 58 MP stroke +gr + +12 w +c10 +0 sg +2376 220 mt +(COMPLEX 1D WAVELET) s +3114 2106 mt +(t) s +%%IncludeResource: font Symbol +/Symbol /ISOLatin1Encoding 132 FMSR + + 399 1168 mt -90 rotate +(y) s +90 rotate +%%IncludeResource: font Helvetica +/Helvetica /ISOLatin1Encoding 132 FMSR + + 399 1078 mt -90 rotate +(\(t\)) s +90 rotate +6 w + +end %%Color Dict + +eplot +%%EndObject + +epage +end + +showpage + +%%Trailer +%%EOF diff --git a/THESE/codes/wave/allcode/figures/dualtree_eg1_figure.png b/THESE/codes/wave/allcode/figures/dualtree_eg1_figure.png new file mode 100644 index 0000000..3883e6c Binary files /dev/null and b/THESE/codes/wave/allcode/figures/dualtree_eg1_figure.png differ diff --git a/THESE/codes/wave/allcode/icplxdual2D.m b/THESE/codes/wave/allcode/icplxdual2D.m new file mode 100644 index 0000000..8862cf7 --- /dev/null +++ b/THESE/codes/wave/allcode/icplxdual2D.m @@ -0,0 +1,40 @@ +function y = icplxdual2D(w, J, Fsf, sf) + +% Inverse Dual-Tree Complex 2D Discrete Wavelet Transform +% +% USAGE: +% y = icplxdual2D(w, J, Fsf, sf) +% INPUT: +% w - wavelet coefficients +% J - number of stages +% Fsf - synthesis filters for final stage +% sf - synthesis filters for preceeding stages +% OUTPUT: +% y - output array +% See cplxdual2D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +for j = 1:J + for m = 1:3 + [w{j}{1}{1}{m} w{j}{2}{2}{m}] = pm(w{j}{1}{1}{m},w{j}{2}{2}{m}); + [w{j}{1}{2}{m} w{j}{2}{1}{m}] = pm(w{j}{1}{2}{m},w{j}{2}{1}{m}); + end +end + +y = zeros(size(w{1}{1}{1}{1})*2); +for m = 1:2 + for n = 1:2 + lo = w{J+1}{m}{n}; + for j = J:-1:2 + lo = sfb2D(lo, w{j}{m}{n}, sf{m}, sf{n}); + end + lo = sfb2D(lo, w{1}{m}{n}, Fsf{m}, Fsf{n}); + y = y + lo; + end +end + +% normalization +y = y/2; + diff --git a/THESE/codes/wave/allcode/icplxdual3D.m b/THESE/codes/wave/allcode/icplxdual3D.m new file mode 100644 index 0000000..f3f3e32 --- /dev/null +++ b/THESE/codes/wave/allcode/icplxdual3D.m @@ -0,0 +1,44 @@ +function y = icplxdual3D(w, J, Fsf, sf) + +% Inverse 3D Complex Dual-Tree Discrete Wavelet Transform +% +% USAGE: +% y = icplxdual3D(w, J, Fsf, sf) +% INPUT: +% J - number of stages +% Fsf - synthesis filter for last stage +% sf - synthesis filters for preceeding stages +% OUTPUT: +% y - output array +% See cplxdual3D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +for j = 1:J + for m = 1:7 + [w{j}{1}{1}{1}{m} w{j}{2}{2}{1}{m} w{j}{2}{1}{2}{m} w{j}{1}{2}{2}{m}] = ... + pm4inv(w{j}{1}{1}{1}{m}, w{j}{2}{2}{1}{m}, w{j}{2}{1}{2}{m}, w{j}{1}{2}{2}{m}); + [w{j}{2}{2}{2}{m} w{j}{1}{1}{2}{m} w{j}{1}{2}{1}{m} w{j}{2}{1}{1}{m}] = ... + pm4inv(w{j}{2}{2}{2}{m}, w{j}{1}{1}{2}{m}, w{j}{1}{2}{1}{m}, w{j}{2}{1}{1}{m}); + end +end + +y = zeros(2*size(w{1}{1}{1}{1}{1})); +for m = 1:2 + for n = 1:2 + for p = 1:2 + lo = w{J+1}{m}{n}{p}; + for j = J:-1:2 + lo = sfb3D(lo, w{j}{m}{n}{p}, sf{m}, sf{n}, sf{p}); + end + lo = sfb3D(lo, w{1}{m}{n}{p}, Fsf{m}, Fsf{n}, Fsf{p}); + y = y + lo; + end + end +end + +% normalization +y = y/sqrt(8); + + diff --git a/THESE/codes/wave/allcode/idualtree.m b/THESE/codes/wave/allcode/idualtree.m new file mode 100644 index 0000000..a4cdf90 --- /dev/null +++ b/THESE/codes/wave/allcode/idualtree.m @@ -0,0 +1,35 @@ +function y = idualtree(w, J, Fsf, sf) + +% Inverse Dual-tree Complex DWT +% +% USAGE: +% y = idualtree(w, J, Fsf, sf) +% INPUT: +% w - DWT coefficients +% J - number of stages +% Fsf - synthesis filters for the last stage +% sf - synthesis filters for preceeding stages +% OUTUT: +% y - output signal +% See dualtree +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% Tree 1 +y1 = w{J+1}{1}; +for j = J:-1:2 + y1 = sfb(y1, w{j}{1}, sf{1}); +end +y1 = sfb(y1, w{1}{1}, Fsf{1}); + +% Tree 2 +y2 = w{J+1}{2}; +for j = J:-1:2 + y2 = sfb(y2, w{j}{2}, sf{2}); +end +y2 = sfb(y2, w{1}{2}, Fsf{2}); + +% normalization +y = (y1 + y2)/sqrt(2); + diff --git a/THESE/codes/wave/allcode/idualtree2D.m b/THESE/codes/wave/allcode/idualtree2D.m new file mode 100644 index 0000000..1826d3e --- /dev/null +++ b/THESE/codes/wave/allcode/idualtree2D.m @@ -0,0 +1,44 @@ +function y = idualtree2D(w, J, Fsf, sf) + +% Inverse 2-D Dual-Tree Discrete Wavelet Transform +% +% USAGE: +% y = idualtree2D(w, J, Fsf, sf) +% INPUT: +% J - number of stages +% Fsf - synthesis filters for final stage +% sf - synthesis filters for preceeding stages +% OUPUT: +% y - output array +% See idualtree2D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +% sum and difference +for k = 1:J + for m = 1:3 + A = w{k}{1}{m}; + B = w{k}{2}{m}; + w{k}{1}{m} = (A+B)/sqrt(2); + w{k}{2}{m} = (A-B)/sqrt(2); + end +end + +% Tree 1 +y1 = w{J+1}{1}; +for j = J:-1:2 + y1 = sfb2D(y1, w{j}{1}, sf{1}); +end +y1 = sfb2D(y1, w{1}{1}, Fsf{1}); + +% Tree 2 +y2 = w{J+1}{2}; +for j = J:-1:2 + y2 = sfb2D(y2, w{j}{2}, sf{2}); +end +y2 = sfb2D(y2, w{1}{2}, Fsf{2}); + +% normalization +y = (y1 + y2)/sqrt(2); + diff --git a/THESE/codes/wave/allcode/idualtree3D.m b/THESE/codes/wave/allcode/idualtree3D.m new file mode 100644 index 0000000..34b7ee6 --- /dev/null +++ b/THESE/codes/wave/allcode/idualtree3D.m @@ -0,0 +1,51 @@ +function y = idualtree3D(w, J, Fsf, sf) + +% Inverse 3D Dual-Tree Discrete Wavelet Transform +% +% USAGE: +% y = idualtree3D(w, J, Fsf, sf) +% INPUT: +% w - wavelet coefficients +% J - number of stages +% Fsf - synthesis filter for the last stage +% sf - synthesis filters for the preceeding stages +% OUTPUT: +% y - output arry +% See dualtree3D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +for k = 1:J + for m = 1:7 + [w{k}{1}{m} w{k}{2}{m} w{k}{3}{m} w{k}{4}{m}] = ... + pm4inv(w{k}{1}{m}, w{k}{2}{m}, w{k}{3}{m}, w{k}{4}{m}); + end +end + +M = [ + 1 1 1 + 2 2 1 + 2 1 2 + 1 2 2 +]; + +% initialize output array +y = zeros(2^J * size(w{J}{1}{1})); + +for i = 1:4 + f1 = M(i,1); + f2 = M(i,2); + f3 = M(i,3); + yi = w{J+1}{i}; + for k = J:-1:2 + yi = sfb3D(yi, w{k}{i}, sf{f1}, sf{f2}, sf{f3}); + end + yi = sfb3D(yi, w{1}{i}, Fsf{f1}, Fsf{f2}, Fsf{f3}); + y = y + yi; +end + +% normalization +y = y/2; + + diff --git a/THESE/codes/wave/allcode/idwt.m b/THESE/codes/wave/allcode/idwt.m new file mode 100644 index 0000000..5b40467 --- /dev/null +++ b/THESE/codes/wave/allcode/idwt.m @@ -0,0 +1,22 @@ +function y = idwt(w, J, sf) + +% Inverse Discrete 1-D Wavelet Transform +% +% USAGE: +% y = idwt(w, J, sf) +% INPUT: +% w - wavelet coefficients +% J - number of stages +% sf - synthesis filters +% OUTPUT: +% y - output signal +% See also dwt +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +y = w{J+1}; +for k = J:-1:1 + y = sfb(y, w{k}, sf); +end + diff --git a/THESE/codes/wave/allcode/idwt2D.m b/THESE/codes/wave/allcode/idwt2D.m new file mode 100644 index 0000000..8ec1a01 --- /dev/null +++ b/THESE/codes/wave/allcode/idwt2D.m @@ -0,0 +1,22 @@ +function y = idwt2D(w, J, sf) + +% Inverse 2-D Discrete Wavelet Transform +% +% USAGE: +% y = idwt(w, J, sf) +% INPUT: +% w - wavelet coefficients +% J - number of stages +% sf - synthesis filters +% OUTPUT: +% y - output array +% See dwt2D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +y = w{J+1}; +for k = J:-1:1 + y = sfb2D(y, w{k}, sf, sf); +end + diff --git a/THESE/codes/wave/allcode/idwt3D.m b/THESE/codes/wave/allcode/idwt3D.m new file mode 100644 index 0000000..c611c65 --- /dev/null +++ b/THESE/codes/wave/allcode/idwt3D.m @@ -0,0 +1,22 @@ +function y = idwt3D(w, J, sf) + +% Inverse 3-D Discrete Wavelet Transform +% +% USAGE: +% y = idwt3D(w, J, sf) +% INPUT: +% w - wavelet coefficient +% J - number of stages +% sf - synthesis filters +% OUTPUT: +% y - output array +% See: dwt3D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +y = w{J+1}; +for k = J:-1:1 + y = sfb3D(y, w{k}, sf, sf, sf); +end + diff --git a/THESE/codes/wave/allcode/lena b/THESE/codes/wave/allcode/lena new file mode 100644 index 0000000..c35133d --- /dev/null +++ b/THESE/codes/wave/allcode/lena @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/THESE/codes/wave/allcode/main_dtdwt.m b/THESE/codes/wave/allcode/main_dtdwt.m new file mode 100644 index 0000000..0329da9 --- /dev/null +++ b/THESE/codes/wave/allcode/main_dtdwt.m @@ -0,0 +1,29 @@ +% Main function +% Usage : +% main_dtdwt +% INPUT : +% Raw Lena image +% OUTPUT : +% PSNR value of the denoised image +% +% Load clean image +fid = fopen('boat','r'); +s = fread(fid,[512 512],'unsigned char'); +fclose(fid); +N = 512; + +% Noise variance +sigma_n = 30; +n = sigma_n*randn(N); + +% Add noise +x = s + n; + +% Run local adaptive image denoising algorithm using dual-tree DWT. +y = denoising_dtdwt(x); + +% Calculate the error +err = s - y; + +% Calculate the PSNR value +PSNR = 20*log10(256/std(err(:))) \ No newline at end of file diff --git a/THESE/codes/wave/allcode/main_dwt.m b/THESE/codes/wave/allcode/main_dwt.m new file mode 100644 index 0000000..dcc15fe --- /dev/null +++ b/THESE/codes/wave/allcode/main_dwt.m @@ -0,0 +1,31 @@ +% Main function +% Usage : +% main +% INPUT : +% Raw Lena image +% OUTPUT : +% PSNR value of the denoised image +% +% Load clean image +fid = fopen('barbara','r'); +s = fread(fid,[512 512],'unsigned char'); +fclose(fid) +%s = imread('zoe.bmp','bmp'); +%s = double(s); +N = 512; + +% Noise variance +sigma_n = 25; +n = sigma_n*randn(N); + +% Add noise +x = s + n; + +% Run local adaptive image denoising algorithm +y = denoising_dwt(x); + +% Calculate the error +err = s - y; + +% Calculate the PSNR value +PSNR = 20*log10(256/std(err(:))) \ No newline at end of file diff --git a/THESE/codes/wave/allcode/nor_dualtree.mat b/THESE/codes/wave/allcode/nor_dualtree.mat new file mode 100644 index 0000000..5fdd8ff Binary files /dev/null and b/THESE/codes/wave/allcode/nor_dualtree.mat differ diff --git a/THESE/codes/wave/allcode/normaliz_coefcalc_dual_tree.m b/THESE/codes/wave/allcode/normaliz_coefcalc_dual_tree.m new file mode 100644 index 0000000..0f79bdd --- /dev/null +++ b/THESE/codes/wave/allcode/normaliz_coefcalc_dual_tree.m @@ -0,0 +1,32 @@ + + +N = 2^9; +L = 6; +x = zeros(N); + +%[Fsf,Faf] = FSfarras; +[Fsf,Faf] = AntonB; +[sf,af] = dualfilt1; + +W_zero = cplxdual2D(x, L, Faf, af); + +figure(1), clf +nor = []; + +for scale = 1:L + no = length(W_zero{scale}{1}{1}{1})/2; + for part = 1:2 + for dir = 1:2 + for dir1 = 1:3 + W = W_zero; + W{scale}{part}{dir}{dir1}(no,no) = 1; + y = icplxdual2D(W, L, Fsf, sf); + nor{scale}{part}{dir}{dir1} = sqrt(sum(sum(y.^2))); + %sqrt(sum(sum(y.^2))) + end + end + end +end + +save nor_dualtree nor + diff --git a/THESE/codes/wave/allcode/normcoef.m b/THESE/codes/wave/allcode/normcoef.m new file mode 100644 index 0000000..4498a0e --- /dev/null +++ b/THESE/codes/wave/allcode/normcoef.m @@ -0,0 +1,12 @@ +function [W]= normcoef(W,L,nor) + + +for scale = 1:L + for part = 1:2 + for dir = 1:2 + for dir1 = 1:3 + W{scale}{part}{dir}{dir1} = W{scale}{part}{dir}{dir1}/nor{scale}{part}{dir}{dir1}; + end + end + end +end diff --git a/THESE/codes/wave/allcode/plotwav1D.m b/THESE/codes/wave/allcode/plotwav1D.m new file mode 100644 index 0000000..e9d992f --- /dev/null +++ b/THESE/codes/wave/allcode/plotwav1D.m @@ -0,0 +1,15 @@ +clear all +close all + +[af, sf] = farras; % analysis and synthesis filter +x=zeros(1,64); % create generic signal whose values are all 0 +w = dwt(x,3,af); % analysis filter banks (3 stages) +w{3}(4)=1; % setting one of the wavelet coefficients to be 1 +y = idwt(w,3,sf); % synthesis filter banks (3 stages) +plot(y); % Plot the wave +title('Standard 1-D wavelet transform') +xlabel('t'); +ylabel('\psi(t)'); + + + diff --git a/THESE/codes/wave/allcode/pm.m b/THESE/codes/wave/allcode/pm.m new file mode 100644 index 0000000..1776fa6 --- /dev/null +++ b/THESE/codes/wave/allcode/pm.m @@ -0,0 +1,9 @@ +function [u, v] = pm(a,b) + +% [u v] = pm(a,b) +% u = (a + b)/sqrt(2); +% v = (a - b)/sqrt(2); + +u = (a + b)/sqrt(2); +v = (a - b)/sqrt(2); + diff --git a/THESE/codes/wave/allcode/pm4.m b/THESE/codes/wave/allcode/pm4.m new file mode 100644 index 0000000..f5fd7ac --- /dev/null +++ b/THESE/codes/wave/allcode/pm4.m @@ -0,0 +1,12 @@ +function [u, v, q, r] = pm4(a, b, c, d) + +% [u, v, q, r] = pm4(a, b, c, d); +% u = (a + b - c - d)/2; +% v = (a - b + c + d)/2; +% q = (a + b - c + d)/2; +% r = (a + b + c - d)/2; + +u = (a - b - c - d)/2; +v = (a - b + c + d)/2; +q = (a + b - c + d)/2; +r = (a + b + c - d)/2; diff --git a/THESE/codes/wave/allcode/pm4inv.m b/THESE/codes/wave/allcode/pm4inv.m new file mode 100644 index 0000000..29b1cab --- /dev/null +++ b/THESE/codes/wave/allcode/pm4inv.m @@ -0,0 +1,12 @@ +function [u, v, q, r] = pm4inv(a, b, c, d) + +% [u, v, q, r] = pm4(a, b, c, d); +% u = (a + b - c - d)/2; +% v = (a - b + c + d)/2; +% q = (a + b - c + d)/2; +% r = (a + b + c - d)/2; + +u = ( a + b + c + d)/2; +v = (-a - b + c + d)/2; +q = (-a + b - c + d)/2; +r = (-a + b + c - d)/2; diff --git a/THESE/codes/wave/allcode/rden2.m b/THESE/codes/wave/allcode/rden2.m new file mode 100644 index 0000000..2849df2 --- /dev/null +++ b/THESE/codes/wave/allcode/rden2.m @@ -0,0 +1,16 @@ +function re = rden2(s,x,t) + +% % Example +% s1 = double(imread('st.tif')); +% s = s1(:,:,3); +% x = s + 20*randn(size(s)); +% t = 0:5:45; +% e = rden2(s,x,t); +% plot(t,e); + +N = length(t); +for k = 1:N + y = denR2D(x,t(k)); + re(k) = sqrt(mean(mean((y-s).^2))); +end + diff --git a/THESE/codes/wave/allcode/sfb.m b/THESE/codes/wave/allcode/sfb.m new file mode 100644 index 0000000..8a6a169 --- /dev/null +++ b/THESE/codes/wave/allcode/sfb.m @@ -0,0 +1,28 @@ +function y = sfb(lo, hi, sf) + +% Synthesis filter bank +% +% USAGE: +% y = sfb(lo, hi, sf) +% INPUT: +% lo - low frqeuency input +% hi - high frequency input +% sf - synthesis filters +% sf(:, 1) - lowpass filter (even length) +% sf(:, 2) - highpass filter (even length) +% OUTPUT: +% y - output signal +% See also afb +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +N = 2*length(lo); +L = length(sf); +lo = upfirdn(lo, sf(:,1), 2, 1); +hi = upfirdn(hi, sf(:,2), 2, 1); +y = lo + hi; +y(1:L-2) = y(1:L-2) + y(N+[1:L-2]); +y = y(1:N); +y = cshift(y, 1-L/2); + diff --git a/THESE/codes/wave/allcode/sfb2D.m b/THESE/codes/wave/allcode/sfb2D.m new file mode 100644 index 0000000..61d7028 --- /dev/null +++ b/THESE/codes/wave/allcode/sfb2D.m @@ -0,0 +1,30 @@ +function y = sfb2D(lo, hi, sf1, sf2) + +% 2D Synthesis Filter Bank +% +% USAGE: +% y = sfb2D(lo, hi, sf1, sf2); +% INPUT: +% lo, hi - lowpass, highpass subbands +% sf1 - synthesis filters for the columns +% sf2 - synthesis filters for the rows +% OUTPUT: +% y - output array +% See afb2D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + + +if nargin < 4 + sf2 = sf1; +end + +% filter along rows +lo = sfb2D_A(lo, hi{1}, sf2, 2); +hi = sfb2D_A(hi{2}, hi{3}, sf2, 2); + +% filter along columns +y = sfb2D_A(lo, hi, sf1, 1); + + diff --git a/THESE/codes/wave/allcode/sfb2D_A.m b/THESE/codes/wave/allcode/sfb2D_A.m new file mode 100644 index 0000000..ad4d144 --- /dev/null +++ b/THESE/codes/wave/allcode/sfb2D_A.m @@ -0,0 +1,30 @@ +function y = sfb2D_A(lo, hi, sf, d) + +% 2D Synthesis Filter Bank +% (along single dimension only) +% +% y = sfb2D_A(lo, hi, sf, d); +% sf - synthesis filters +% d - dimension of filtering +% see afb2D_A + + +lpf = sf(:, 1); % lowpass filter +hpf = sf(:, 2); % highpass filter + +if d == 2 + lo = lo'; + hi = hi'; +end + +N = 2*size(lo,1); +L = length(sf); +y = upfirdn(lo, lpf, 2, 1) + upfirdn(hi, hpf, 2, 1); +y(1:L-2, :) = y(1:L-2, :) + y(N+[1:L-2], :); +y = y(1:N, :); +y = cshift2D(y, 1-L/2); + +if d == 2 + y = y'; +end + diff --git a/THESE/codes/wave/allcode/sfb3D.m b/THESE/codes/wave/allcode/sfb3D.m new file mode 100644 index 0000000..95c998f --- /dev/null +++ b/THESE/codes/wave/allcode/sfb3D.m @@ -0,0 +1,80 @@ +function y = sfb3D(lo, hi, sf1, sf2, sf3) + +% 3D Synthesis Filter Bank +% +% USAGE: +% y = sfb3D(lo, hi, sf1, sf2, sf3); +% INPUT: +% lo, hi - lowpass subbands +% sfi - synthesis filters for dimension i +% OUPUT: +% y - output array +% See afb3D +% +% WAVELET SOFTWARE AT POLYTECHNIC UNIVERSITY, BROOKLYN, NY +% http://taco.poly.edu/WaveletSoftware/ + +if nargin < 4 + sf2 = sf1; + sf3 = sf1; +end + +LLL = lo; +LLH = hi{1}; +LHL = hi{2}; +LHH = hi{3}; +HLL = hi{4}; +HLH = hi{5}; +HHL = hi{6}; +HHH = hi{7}; + +% filter along dimension 3 +LL = sfb3D_A(LLL, LLH, sf3, 3); +LH = sfb3D_A(LHL, LHH, sf3, 3); +HL = sfb3D_A(HLL, HLH, sf3, 3); +HH = sfb3D_A(HHL, HHH, sf3, 3); + +% filter along dimension 3 +L = sfb3D_A(LL, LH, sf2, 2); +H = sfb3D_A(HL, HH, sf2, 2); + +% filter along dimension 1 +y = sfb3D_A(L, H, sf1, 1); + + +% LOCAL FUNCTION + +function y = sfb3D_A(lo, hi, sf, d) + +% 3D Synthesis Filter Bank +% (along single dimension only) +% +% y = sfb3D_A(lo, hi, sf, d); +% sf - synthesis filters +% d - dimension of filtering +% see afb2D_A + +lpf = sf(:, 1); % lowpass filter +hpf = sf(:, 2); % highpass filter + +% permute dimensions of lo and hi so that dimension d is first. +p = mod(d-1+[0:2], 3) + 1; +lo = permute(lo, p); +hi = permute(hi, p); + +[N1, N2, N3] = size(lo); +N = 2*N1; +L = length(sf); +y = zeros(N+L-2, N2, N3); + +for k = 1:N3 + y(:, :, k) = upfirdn(lo(:, :, k), lpf, 2, 1) + upfirdn(hi(:, :, k), hpf, 2, 1); +end +y(1:L-2, :, :) = y(1:L-2, :, :) + y(N+[1:L-2], :, :); +y = y(1:N, :, :); +y = cshift3D(y, 1-L/2, 1); + +% permute dimensions of y (inverse permutation) +y = ipermute(y, p); + + diff --git a/THESE/codes/wave/allcode/soft.m b/THESE/codes/wave/allcode/soft.m new file mode 100644 index 0000000..10935d0 --- /dev/null +++ b/THESE/codes/wave/allcode/soft.m @@ -0,0 +1,5 @@ +function y = soft(x,T) + +y = max(abs(x) - T, 0); +y = y./(y+T) .* x; + diff --git a/THESE/codes/wave/allcode/st.tif b/THESE/codes/wave/allcode/st.tif new file mode 100644 index 0000000..52db04b Binary files /dev/null and b/THESE/codes/wave/allcode/st.tif differ diff --git a/THESE/codes/wave/allcode/symextend.m b/THESE/codes/wave/allcode/symextend.m new file mode 100644 index 0000000..f0bc429 --- /dev/null +++ b/THESE/codes/wave/allcode/symextend.m @@ -0,0 +1,6 @@ +function [y] = symextend(x,Nnum) + +y(:,1:Nnum) = fliplr(x(:,1:Nnum)); +y = [fliplr(x(:,1:Nnum)) x x(:,end:-1:end-Nnum+1)]; +y = [flipud(y(1:Nnum,:)); y ;y(end:-1:end-Nnum+1,:)]; + diff --git a/THESE/codes/wave/allcode/tee.mat b/THESE/codes/wave/allcode/tee.mat new file mode 100644 index 0000000..a9e6482 Binary files /dev/null and b/THESE/codes/wave/allcode/tee.mat differ diff --git a/THESE/codes/wave/allcode/unnormcoef.m b/THESE/codes/wave/allcode/unnormcoef.m new file mode 100644 index 0000000..ae3f492 --- /dev/null +++ b/THESE/codes/wave/allcode/unnormcoef.m @@ -0,0 +1,12 @@ +function [W]= normcoef(W,L,nor) + + +for scale = 1:L + for part = 1:2 + for dir = 1:2 + for dir1 = 1:3 + W{scale}{part}{dir}{dir1} = W{scale}{part}{dir}{dir1}*nor{scale}{part}{dir}{dir1}; + end + end + end +end 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C'est le contour final.}}}{21}{figure.2.5} +\contentsline {figure}{\numberline {2.1}{\ignorespaces Images 256$\times $256 en niveau de gris 8 bits utilis\IeC {\'e}es pour l'illustration des propri\IeC {\'e}t\IeC {\'e}s des filtres. a) l'image de r\IeC {\'e}f\IeC {\'e}rence non bruit\IeC {\'e}e. b) l'image corrompue par un bruit gaussien d'\IeC {\'e}cart type $\sigma =25$. c) l'image corrompue par un bruit impulsionnel \IeC {\`a} 25\%.}}{14}{figure.2.1} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Sans bruit}}}{14}{figure.2.1} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Bruit gaussien $\sigma =25$}}}{14}{figure.2.1} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Bruit impulsionnel 25\%}}}{14}{figure.2.1} +\contentsline {figure}{\numberline {2.2}{\ignorespaces .}}{15}{figure.2.2} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Moyenneur 3$\times $3}}}{15}{figure.2.2} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Moyenneur 5$\times $5}}}{15}{figure.2.2} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {D\IeC {\'e}rivateur horizontal}}}{15}{figure.2.2} +\contentsline {figure}{\numberline {2.3}{\ignorespaces R\IeC {\'e}duction du bruit impulsionnel par filtre m\IeC {\'e}dian.}}{15}{figure.2.3} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {M\IeC {\'e}dian 3$\times $3 une passe}}}{15}{figure.2.3} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {M\IeC {\'e}dian 3$\times $3 deux passes}}}{15}{figure.2.3} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {M\IeC {\'e}dian 5$\times $5 une passe}}}{15}{figure.2.3} +\contentsline {figure}{\numberline {2.4}{\ignorespaces R\IeC {\'e}duction de bruit gaussien par filtrage bilat\IeC {\'e}ral gaussien de voisinage 5$\times $5. $\sigma _S$ et $\sigma _I$ sont les \IeC {\'e}carts type des fonctions de pond\IeC {\'e}ration spatiale et d'intensit\IeC {\'e}.}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {$\sigma _S=1.0$ et $\sigma _I=0.1$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {$\sigma _S=1.0$ et $\sigma _I=0.5$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {$\sigma _S=1.0$ et $\sigma _I=1.0$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {$\sigma _S=2.0$ et $\sigma _I=0.1$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {$\sigma _S=2.0$ et $\sigma _I=0.5$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {$\sigma _S=2.0$ et $\sigma _I=1.0$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(g)}{\ignorespaces {$\sigma _S=5.0$ et $\sigma _I=0.1$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(h)}{\ignorespaces {$\sigma _S=5.0$ et $\sigma _I=0.5$}}}{17}{figure.2.4} +\contentsline {subfigure}{\numberline {(i)}{\ignorespaces {$\sigma _S=5.0$ et $\sigma _I=1.0$}}}{17}{figure.2.4} +\contentsline {figure}{\numberline {2.5}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par analyse simple d'histogramme. Colonne de gauche : image d'entr\IeC {\'e}e. Colonne centrale : histogramme des niveaux de gris. Colonne de droite : r\IeC {\'e}sultat de la segmentation.}}{21}{figure.2.5} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image initiale comportant deux zones : le fond et le cochon (la cible)}}}{21}{figure.2.5} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Histogramme des niveaux de gris}}}{21}{figure.2.5} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Image binaire repr\IeC {\'e}sentant la segmentation. Seuil estim\IeC {\'e} \IeC {\`a} 101 apr\IeC {\`e}s 4 it\IeC {\'e}rations.}}}{21}{figure.2.5} +\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Image initiale bruit\IeC {\'e}e}}}{21}{figure.2.5} +\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Histogramme des niveaux de gris}}}{21}{figure.2.5} +\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Image binaire repr\IeC {\'e}sentant la segmentation. Seuil estim\IeC {\'e} \IeC {\`a} 99 apr\IeC {\`e}s 5 it\IeC {\'e}rations.}}}{21}{figure.2.5} +\contentsline {figure}{\numberline {2.6}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par simplification de graphe de type \textit {Normalized cut} pour un nombre $s$ de segments variant de 2 \IeC {\`a} 5.}}{23}{figure.2.6} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {$s = 2$}}}{23}{figure.2.6} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {$s = 3$}}}{23}{figure.2.6} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {$s = 4$}}}{23}{figure.2.6} +\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {$s = 5$}}}{23}{figure.2.6} +\contentsline {figure}{\numberline {2.7}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par algorithme \textit {k-means} pour un nombre $s$ de segments variant de 2 \IeC {\`a} 5. Chaque couleur est associ\IeC {\'e}e \IeC {\`a} un segment. Les couleurs sont choisies pour une meilleure visualisation des diff\IeC {\'e}rents segments.}}{24}{figure.2.7} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {$s = 2$}}}{24}{figure.2.7} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {$s = 3$}}}{24}{figure.2.7} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {$s = 4$}}}{24}{figure.2.7} +\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {$s = 5$}}}{24}{figure.2.7} +\contentsline {figure}{\numberline {2.8}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par algorithme \textit {mean-shift} pour un rayon de voisinage $r$ de 100, 50, 35 et 25 pixels permettant d'obtenir un nombre $s$ de segments variant respectivement de 2 \IeC {\`a} 5. Le volume minimal admis pour un segment est fix\IeC {\'e} \IeC {\`a} 100 pixels. Chaque couleur est associ\IeC {\'e}e \IeC {\`a} un segment. Les couleurs sont choisies pour une meilleure visualisation des diff\IeC {\'e}rents segments.}}{24}{figure.2.8} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {$r=100 \Rightarrow s = 2$}}}{24}{figure.2.8} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {$r=50 \Rightarrow s = 3$}}}{24}{figure.2.8} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {$r=35 \Rightarrow s = 4$}}}{24}{figure.2.8} +\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {$r=25 \Rightarrow s = 5$}}}{24}{figure.2.8} +\contentsline {figure}{\numberline {2.9}{\ignorespaces Segmentation d'une image en niveaux de gris de 128 $\times $ 128 pixels par algorithme dit du \textit {snake}, dans sa version originale. Les param\IeC {\`e}tres d'\IeC {\'e}lasticti\IeC {\'e}, de raideur et d'attraction ont \IeC {\'e}t\IeC {\'e} fix\IeC {\'e}s respectivement aux valeurs 5, 0.1 et 5. }}{26}{figure.2.9} +\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Les \IeC {\'e}tats initial et suivant chacune des trois premi\IeC {\`e}res it\IeC {\'e}rations}}}{26}{figure.2.9} +\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {L'\IeC {\'e}tat du contour apr\IeC {\`e}s la septi\IeC {\`e}me it\IeC {\'e}ration}}}{26}{figure.2.9} +\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {L'\IeC {\'e}tat du contour apr\IeC {\`e}s la dixi\IeC {\`e}me it\IeC {\'e}ration}}}{26}{figure.2.9} +\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {L'\IeC {\'e}tat du contour apr\IeC {\`e}s la centi\IeC {\`e}me it\IeC {\'e}ration. 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C'est le [] -[20 ] [21 ] +[25] -LaTeX Warning: Reference `level_sets_osher_sethian_1988' on page 22 undefined o -n input line 220. +LaTeX Warning: Reference `level_sets_osher_sethian_1988' on page 26 undefined o +n input line 333. -LaTeX Warning: Reference `narrow_band_level_set' on page 22 undefined on input -line 220. +LaTeX Warning: Reference `narrow_band_level_set' on page 26 undefined on input +line 333. -LaTeX Warning: Reference `fast_marching_sethian' on page 22 undefined on input -line 220. +LaTeX Warning: Reference `fast_marching_sethian' on page 26 undefined on input +line 333. -LaTeX Warning: Reference `cohenSMIE93, ronfard' on page 22 undefined on input l -ine 221. +LaTeX Warning: Reference `cohenSMIE93, ronfard' on page 26 undefined on input l +ine 334. ! Undefined control sequence. -l.221 ...ibitifs jusqu'à ce que Bertaux \textitat +l.334 ...ibitifs jusqu'à ce que Bertaux \textitat {et al.} proposent une amÃ... The control sequence at the end of the top line of your error message was never \def'ed. If you have @@ -1426,62 +1698,70 @@ spelling (e.g., `I\hbox'). Otherwise just continue, and I'll forget about whatever was undefined. -LaTeX Warning: Reference `snake_bertaux' on page 22 undefined on input line 221 +LaTeX Warning: Reference `snake_bertaux' on page 26 undefined on input line 334 . -LaTeX Warning: Reference `sec_contrib_snake' on page 22 undefined on input line - 221. +LaTeX Warning: Reference `sec_contrib_snake' on page 26 undefined on input line + 334. -LaTeX Warning: Reference `amfm_2010' on page 22 undefined on input line 227. +LaTeX Warning: Reference `amfm_2010' on page 26 undefined on input line 340. -LaTeX Warning: Reference `watershed' on page 22 undefined on input line 227. +LaTeX Warning: Reference `watershed' on page 26 undefined on input line 340. -LaTeX Warning: Reference `sec_ea_gpu' on page 22 undefined on input line 228. +LaTeX Warning: Reference `sec_ea_gpu' on page 26 undefined on input line 341. -) [22] -Chapitre 3. -[23 -] [24 +Underfull \vbox (badness 10000) has occurred while \output is active [] + + [26 ]) [27] + [28 + +] +Chapitre 3. +[29] [30 ] Chapitre 4. -[25] [26 +[31] [32 ] Chapitre 5. No file these.bbl. -[27] [28 +[33] [34 ] (./these.lof) \tf@lof=\write5 \openout5 = `these.lof'. - [29] [30 + [35] [36 ] (./these.lot) \tf@lot=\write6 \openout6 = `these.lot'. - [31] [32 + [37] [38 -] [33] +] [39] pdfTeX warning: pdflatex (file /usr/share/texmf/tex/latex/upmethodology-extensi ons/phd_thesis/spimufcphdthesis/spimufcphdthesis-backpage.pdf): PDF inclusion: found PDF version <1.6>, but at most version <1.4> allowed - + File: spimufcphdthesis-backpage.pdf Graphic file (type pdf) LaTeX Font Info: Font shape `OT1/phv/bx/n' in size <12> not available (Font) Font shape `OT1/phv/b/n' tried instead on input line 130. - [34 + [40 ] @@ -1492,13 +1772,13 @@ LaTeX Warning: There were undefined references. ) (\end occurred when \iftrue on line 130 was incomplete) Here is how much of TeX's memory you used: - 10017 strings out of 495028 - 146837 string characters out of 1181229 - 275496 words of memory out of 3000000 - 12777 multiletter control sequences out of 15000+50000 + 10173 strings out of 495028 + 153868 string characters out of 1181229 + 273131 words of memory out of 3000000 + 12878 multiletter control sequences out of 15000+50000 71443 words of font info for 129 fonts, out of 3000000 for 9000 28 hyphenation exceptions out of 8191 - 61i,15n,47p,1412b,463s stack positions out of 5000i,500n,10000p,200000b,50000s + 61i,15n,47p,1412b,505s stack positions out of 5000i,500n,10000p,200000b,50000s {/usr/share/texmf-texliv e/fonts/enc/dvips/base/8r.enc} -Output written on these.pdf (34 pages, 3871347 bytes). +Output written on these.pdf (40 pages, 4371225 bytes). PDF statistics: - 494 PDF objects out of 1000 (max. 8388607) - 100 named destinations out of 1000 (max. 500000) - 318 words of extra memory for PDF output out of 10000 (max. 10000000) + 602 PDF objects out of 1000 (max. 8388607) + 134 named destinations out of 1000 (max. 500000) + 448 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/THESE/these.out b/THESE/these.out index df86ba5..a0cf24c 100644 --- a/THESE/these.out +++ b/THESE/these.out @@ -7,13 +7,18 @@ \BOOKMARK [2][]{subsection.2.2.3}{2.2.3 Le bruit ``sel et poivre''}{section.2.2} \BOOKMARK [2][]{subsection.2.2.4}{2.2.4 Le bruit de Poisson}{section.2.2} \BOOKMARK [1][]{section.2.3}{2.3 Les techniques de r\351duction de bruit}{chapter.2} -\BOOKMARK [1][]{section.2.4}{2.4 Les techniques de segmentation}{chapter.2} -\BOOKMARK [2][]{subsection.2.4.1}{2.4.1 Analyse d'histogramme}{section.2.4} -\BOOKMARK [2][]{subsection.2.4.2}{2.4.2 Analyse de graphe}{section.2.4} -\BOOKMARK [2][]{subsection.2.4.3}{2.4.3 kernel-means, mean-shift et d\351riv\351s}{section.2.4} -\BOOKMARK [2][]{subsection.2.4.4}{2.4.4 Les contours actifs, ou snakes}{section.2.4} -\BOOKMARK [2][]{subsection.2.4.5}{2.4.5 M\351thodes hybrides}{section.2.4} -\BOOKMARK [1][]{section.2.5}{2.5 L'\351tat de l'art des impl\351mentations GPU}{chapter.2} +\BOOKMARK [2][]{subsection.2.3.1}{2.3.1 Les op\351rateurs de base}{section.2.3} +\BOOKMARK [3][]{subsubsection.2.3.1.1}{2.3.1.1 Les algorithmes de voisinage}{subsection.2.3.1} +\BOOKMARK [3][]{subsubsection.2.3.1.2}{2.3.1.2 Les algorithmes par dictionnaire}{subsection.2.3.1} +\BOOKMARK [2][]{subsection.2.3.2}{2.3.2 Les techniques avanc\351es}{section.2.3} +\BOOKMARK [1][]{section.2.4}{2.4 Les impl\351mentations GPU des algorithmes de filtrage}{chapter.2} +\BOOKMARK [1][]{section.2.5}{2.5 Les techniques de segmentation}{chapter.2} +\BOOKMARK [2][]{subsection.2.5.1}{2.5.1 Analyse d'histogramme}{section.2.5} +\BOOKMARK [2][]{subsection.2.5.2}{2.5.2 Analyse de graphe}{section.2.5} +\BOOKMARK [2][]{subsection.2.5.3}{2.5.3 kernel-means, mean-shift et d\351riv\351s}{section.2.5} +\BOOKMARK [2][]{subsection.2.5.4}{2.5.4 Les contours actifs, ou snakes}{section.2.5} +\BOOKMARK [2][]{subsection.2.5.5}{2.5.5 M\351thodes hybrides}{section.2.5} +\BOOKMARK [1][]{section.2.6}{2.6 L'\351tat de l'art des impl\351mentations GPU}{chapter.2} \BOOKMARK [0][]{chapter.3}{3 La segmentation orient\351e r\351gions dans les images bruit\351es}{} \BOOKMARK [1][]{section.3.1}{3.1 Pr\351sentation - existant}{chapter.3} \BOOKMARK [1][]{section.3.2}{3.2 La parall\350lisation du snake polygonal}{chapter.3} diff --git a/THESE/these.pdf b/THESE/these.pdf index c832624..887722f 100644 Binary files a/THESE/these.pdf and b/THESE/these.pdf differ diff --git a/THESE/these.tex b/THESE/these.tex index a483507..6cb7ce9 100644 --- a/THESE/these.tex +++ b/THESE/these.tex @@ -74,24 +74,24 @@ \begin{document} \tableofcontents - + %-------------------- % The content of the PhD thesis % objectifs, cadre % annonce du plan \chapter{Introduction} \input{Chapters/chapter1/chapter1.tex} -\chapter{Le traitement des images bruitées} +\chapter{Le traitement des images bruitées} \input{Chapters/chapter2/chapter2.tex} % généraliéts %classes d'algo, outils ( histo, PDF, gauss, speckle, etc..) \section{L'état de l'art des implémentations GPU} % segmentation -% filtrage : réduction de bruit, convolutions -% tracking +% filtrage : réduction de bruit, convolutions +% tracking % pattern recognition \chapter{La segmentation orientée régions dans les images bruitées} -\section{Présentation - existant} +\section{Présentation - existant} % segmentation, intérêt, domaines % notations, formules % snake polygonal diff --git a/THESE/these.toc b/THESE/these.toc index 5286c63..4753c36 100644 --- a/THESE/these.toc +++ b/THESE/these.toc @@ -8,18 +8,23 @@ \contentsline {subsection}{\numberline {2.2.3}Le bruit ``sel et poivre''}{13}{subsection.2.2.3} \contentsline {subsection}{\numberline {2.2.4}Le bruit de Poisson}{13}{subsection.2.2.4} \contentsline {section}{\numberline {2.3}Les techniques de r\IeC {\'e}duction de bruit}{13}{section.2.3} -\contentsline {section}{\numberline {2.4}Les techniques de segmentation}{14}{section.2.4} -\contentsline {subsection}{\numberline {2.4.1}Analyse d'histogramme}{14}{subsection.2.4.1} -\contentsline {subsection}{\numberline {2.4.2}Analyse de graphe}{15}{subsection.2.4.2} -\contentsline {subsection}{\numberline {2.4.3}kernel-means, mean-shift et d\IeC {\'e}riv\IeC {\'e}s}{17}{subsection.2.4.3} -\contentsline {subsection}{\numberline {2.4.4}Les contours actifs, ou \textit {snakes}}{20}{subsection.2.4.4} -\contentsline {subsection}{\numberline {2.4.5}M\IeC {\'e}thodes hybrides}{22}{subsection.2.4.5} -\contentsline {section}{\numberline {2.5}L'\IeC {\'e}tat de l'art des impl\IeC {\'e}mentations GPU}{22}{section.2.5} -\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{23}{chapter.3} -\contentsline {section}{\numberline {3.1}Pr\IeC {\'e}sentation - existant}{23}{section.3.1} -\contentsline {section}{\numberline {3.2}La parall\IeC {\`e}lisation du snake polygonal}{23}{section.3.2} -\contentsline {chapter}{\numberline {4}Le filtrage des images sur GPU}{25}{chapter.4} -\contentsline {section}{\numberline {4.1}Algorithme de r\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{25}{section.4.1} -\contentsline {section}{\numberline {4.2}Filtre m\IeC {\'e}dian}{25}{section.4.2} -\contentsline {section}{\numberline {4.3}Filtres de convolution}{25}{section.4.3} -\contentsline {chapter}{\numberline {5}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{27}{chapter.5} +\contentsline {subsection}{\numberline {2.3.1}Les op\IeC {\'e}rateurs de base}{14}{subsection.2.3.1} +\contentsline {subsubsection}{\numberline {2.3.1.1}Les algorithmes de voisinage}{14}{subsubsection.2.3.1.1} +\contentsline {subsubsection}{\numberline {2.3.1.2}Les algorithmes par dictionnaire}{16}{subsubsection.2.3.1.2} +\contentsline {subsection}{\numberline {2.3.2}Les techniques avanc\IeC {\'e}es}{16}{subsection.2.3.2} +\contentsline {section}{\numberline {2.4}Les impl\IeC {\'e}mentations GPU des algorithmes de filtrage}{18}{section.2.4} +\contentsline {section}{\numberline {2.5}Les techniques de segmentation}{19}{section.2.5} +\contentsline {subsection}{\numberline {2.5.1}Analyse d'histogramme}{20}{subsection.2.5.1} +\contentsline {subsection}{\numberline {2.5.2}Analyse de graphe}{20}{subsection.2.5.2} +\contentsline {subsection}{\numberline {2.5.3}kernel-means, mean-shift et d\IeC {\'e}riv\IeC {\'e}s}{22}{subsection.2.5.3} +\contentsline {subsection}{\numberline {2.5.4}Les contours actifs, ou \textit {snakes}}{25}{subsection.2.5.4} +\contentsline {subsection}{\numberline {2.5.5}M\IeC {\'e}thodes hybrides}{27}{subsection.2.5.5} +\contentsline {section}{\numberline {2.6}L'\IeC {\'e}tat de l'art des impl\IeC {\'e}mentations GPU}{27}{section.2.6} +\contentsline 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