From 325a48b299087c4de2b812fd0f9f1c832beb5a2e Mon Sep 17 00:00:00 2001 From: xdrm-brackets Date: Mon, 28 Sep 2015 11:33:17 +0200 Subject: [PATCH] =?UTF-8?q?M=C3=A9thode=202=20bruit=20additif=20unset,=20?= =?UTF-8?q?=C3=A0=20finir?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/AdditiveNoise.bmp | Bin 263222 -> 263222 bytes code/new.bmp | Bin 263222 -> 263222 bytes code/tests.py | 3 +- code/utility/Additive_Noise.py | 106 ++++++++ dossier/.dossier.tex.swp | 438 --------------------------------- dossier/dossier.pdf | Bin 1357503 -> 1401813 bytes dossier/dossier.tex | 6 +- 7 files changed, 111 insertions(+), 442 deletions(-) delete mode 100644 dossier/.dossier.tex.swp diff --git a/code/AdditiveNoise.bmp b/code/AdditiveNoise.bmp index 7bf8af4a84a335830884eb64d3418f75db69a86c..ec6e65acfb3eb6de3a0cbc92c235fd608fdcbf0b 100644 GIT binary patch literal 263222 zcmYJaXIP@?nl1cJI+Mem-FtU;b$3;@mSq6RK`-$A1j`$)EfQ 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zZ~ue%zq?`o_x64n|G&EbrS3ubf9k9JUpfDJO#fH$|Lgg`RHzz=`+w@I_%KL39i|Nq$e&mVuNlK-o|p8xCm jEBXJ6`>zY|MgGTEe?9(xasR39KF|MO-2c><`Tzd|4>*q% diff --git a/code/tests.py b/code/tests.py index e1645b0..2d862a9 100644 --- a/code/tests.py +++ b/code/tests.py @@ -338,7 +338,8 @@ def testAdditiveNoise(seuilA=10, seuilB=35): print "| Removing Additive |",; t.reset(); - img.content.map = FX.Additive.unset(img.content.map, seuil=seuilB) + # img.content.map = FX.Additive.unset(img.content.map, seuil=seuilB) + img.content.map = FX.Additive.unset2(img.content.map, seuil=seuilB) print "%s |" % (t.get()) # Unparsing diff --git a/code/utility/Additive_Noise.py b/code/utility/Additive_Noise.py index 445112c..3d9d18c 100644 --- a/code/utility/Additive_Noise.py +++ b/code/utility/Additive_Noise.py @@ -129,4 +129,110 @@ class Additive_Noise: if rgbInterval > seuil: cleanMatrix[y][x].setRGB(rMoy, gMoy, bMoy); + return cleanMatrix; + + + + + + # Applique le débruitage de type "Additif" sur la matrice de pixels # + ##################################################################### + # @param pixelMap Matrice de pixel à traiter (modifier) + # @param seuil Seuil de "poids statistique" à partir duquel on doit traiter les pixels compris entre 0 et 100 + # + # @return cleanMatrix matrice propre qui est retournée + def unset2(self, pixelMap, seuil=10): + width = len( pixelMap[0] ) + height = len( pixelMap ) + + # matrice qui sera retournée + cleanMatrix = [] + + while seuil >= 1: # si le seuil n'est pas un pourcentage, on le met en pourcentage + seuil /= 100.0; + + seuil *= 256*8 + + + + # on parcourt tout les pixels + for y in range(0, len(pixelMap)): + cleanMatrix.append( [] ); + for x in range(0, len(pixelMap[y])): + + # on ajoute le pixel à la matrice "propre" + cleanMatrix[y].append( RGBPixel( + r = pixelMap[y][x].r, + g = pixelMap[y][x].g, + b = pixelMap[y][x].b, + x = pixelMap[y][x].x, + y = pixelMap[y][x].y, + bpp = pixelMap[y][x].bpp, + )); + + # on calcule la moyenne des valeurs R G B du pixel courant + pMoy = ( pixelMap[y][x].r + pixelMap[y][x].g + pixelMap[y][x].b ) / 3 + + xmin, ymin, xmax, ymax = x, y, x, y; # les bornes ducarré 3x3 autour du pixel + rMoy, gMoy, bMoy, count = 0.0, 0.0, 0.0, 0 # initialisation des variables de moyennes et de total + rInterval, gInterval, bInterval, rgbInterval = 0, 0, 0, 0 # initialisation des variables d'intervalles entre les couleurs + + + # GESTION DES ANGLES + + # ordonnées: borne inférieure + if y-1 > -1: + ymin = y-1 + # ordonnées: borne supérieure + if y+1 < height: + ymax = y+1 + # abscisses: borne inférieure + if x-1 > -1: + xmin = x-1 + # abscisses: borne supérieure + if x+1 < width: + xmax = x+1 + + + + # contiendra la matrice M' des poids statistiques + neighboursAbsoluteDiff = [] + + # on parcourt le carré de 3x3 + for j in pixelMap[ymin:ymax+1]: + for pix in j[xmin:xmax+1]: + # si le pixel n'est pas le pixel courant (mais ses voisins) et que sa couleur n'est pas trop éloignée des autres + if pix != pixelMap[y][x]: + # calcul de la moyenne autour du pixel + rMoy += pix.r; + gMoy += pix.g; + bMoy += pix.b; + count += 1 + # ajout aux poids statistiques + neighboursAbsoluteDiff.append( abs(pix.r+pix.g+pix.b - pMoy)/3 ); + + # on garde que la moitié la plus petite + statisticWeight = 0; + + neighboursAbsoluteDiff.sort() # on trie la liste + + # on récupère la somme de la moitié des éléments les plus petits (car triée) + for infVal in range(0, (3**2 - 1)/2): + if infVal >= len(neighboursAbsoluteDiff)-1: # si liste vide on arrête + break; + # on effectue la somme + statisticWeight += neighboursAbsoluteDiff[infVal] + + + # si il y a au moins un pixel autour (normalement tjs mais évite l'erreur div par zéro) + if count > 0: + # on calcule les moyennes somme(xi) / n + rMoy = int( rMoy / count ) + gMoy = int( gMoy / count ) + bMoy = int( bMoy / count ) + + # si la couleur est trop "différente" (dépend du seuil) alors on remplace sa couleur par la moyenne des couleurs alentours + if statisticWeight < seuil: + cleanMatrix[y][x].setRGB(rMoy, gMoy, bMoy); + return cleanMatrix; \ No newline at end of file diff --git a/dossier/.dossier.tex.swp b/dossier/.dossier.tex.swp deleted file mode 100644 index ec9a670..0000000 --- a/dossier/.dossier.tex.swp +++ /dev/null @@ -1,438 +0,0 @@ -\documentclass{article} -\usepackage[utf8]{inputenc} -\usepackage[usenames,dvipsnames,svgnames,table]{xcolor} -\usepackage{amsmath,amsfonts} -\usepackage{lmodern} -\usepackage{graphicx} % permet l'insertion d'images -\usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm]{geometry} - -% Définition des symboles -\newcommand{\N}{\mathbb{N}} % \N pour les entiers naturels -\newcommand{\R}{\mathbb{R}} % \R pour les réels -\newcommand\V[1]{\overrightarrow{#1}} % $\V{AB}$ pour les vecteurs - -\title{TRAITEMENT D'IMAGE\\Débruitage} -\author{Adrien MARQUÈS - Alexis HELSON } -\date{Septembre 2015} - -\begin{document} - - \maketitle - - % ======================== % - % === PART 1 - PRELUDE === % - % ======================== % - \part*{Prélude} - - % ====================== % - % === SECT 1 - INTRO === % - % ====================== % - \section{Introduction} - De nombreuses images numériques sont produites aujourd'hui : imagerie médicales, images grand public (appareils photos numériques), numérisation de films ancienc, etc... Une problématique qui se pose est le débruitage des images obtenues. Nous proposons dans ce dossier d'aborder certaines techniques utilisées.\\ - \textbf{Image numérique}\par - Une image numérique de taille \begin{math}n_{x} \times n_{y}\end{math} est composée de pixels (le nombre de pixels est \begin{math}n = n_{x}.n_{y}\end{math}), et pour chaque pixel on dispose (selon les cas) :\\ - - une valeur entière comprise entre 0 et 255 (image en niveaux de gris codée en 8 bits)\\ - - trois valeurs entières comprises entre 0 et 255, correspondant aux trois canaux de couleur (RGB)\\ - - une valeur entière codée sur 12 ou 16 bits, ou même sur 1 seul, mais ces cas ne seront pas traités ici.\\ - \textbf{Bruit}\par - Dans une image numérique, les processus de dégradation peuvent provenir de plusieurs sources, et donner du bruit qui peut avoir différentes expressions mathématiques. Notons \emph{u} l'image d'origine (l'image <>) et \begin{math}u^{OBS}\end{math} l'image mesurée. Les exemples traités dans ce dossiers sont décrits ci-dessous.\par - \indent{ - - l'image \begin{math}u^{OBS}\end{math} est mesurée par des appareils électroniques qui sont perturbés par des fluctuations aléatoires, et ces perturbations viennent s'ajouter au signal mesuré. On parle de \emph{bruit addifit}, et on a \begin{math}u^{OBS} = u + n,\end{math} où \emph{n} est le bruit ajouté. - }\\ - \indent{ - - l'image \begin{math}u^{OBS}\end{math} comporte certains pixels où la valeur est aberrante (par exemple à cause d'un capteur défectueux, ou de défaut de transmission de l'information). Pour fixer les idées, on parle de \emph{bruit <>} lorsque certains pixels (au hasard) ont un niveau 0, et certains ont un niveau 255. - }\\ - \indent{ - - lorsque le processus de formation de l'image comporte des compteurs de photons ou des photomultiplicateurs, lerreur commise peut être proportionnelle au signal mesurée. On parle alors de \emph{bruit multiplicatif} et on a \begin{math}u^{OBS} = u.(1+n)\end{math}. - }\\ - - - \hrulefill\\ - - Cette étude vise à améliorer les méthodes de \emph{nettoyage d'image}, et donc de développer un programme universel de débruitage, c'est-à-dire qu'il réduirait convenablement tout type de bruit, dépendant toutefois de paramètres adaptés à chacun. - \begin{center} \emph{Avant d'essayer d'améliorer quelque chose, il faut d'abord le comprendre.} \end{center} - Nous avons donc commencé par lire des études sur débruitage, la détection de contours, de formes, et la vectorisation d'images. - \noindent Pour cela, nous avont étudié: - \begin{description} - \item[ - Le débruitage standard,]c'est à dire les méthodes déjà existantes. - \item[ - Le filtrage par convolution,]afin de mettre en valeur les contours et formes et d'effectuer des "lissages". - \item[ - La détection de formes,]afin d'effectuer un débruitage sans <>. - \item[ - La détection de contours,]afin d'optimiser la détection de formes. - \end{description} - Le programme a été développé en Python2 pour la simplicité et efficacité du langage. Le code est disponible sur Github à l'adresse suivante: - \begin{center} \textcolor{blue}{https://github.com/xdrm-brackets/bmp\_python} \end{center} - - - - - - % =========================== % - % === SECT 2 - ARCHI PROG === % - % =========================== % - \section{Architecture et structure du programme} - - \begin{figure}[!ht] - \centering - \includegraphics{fileTree.jpg} - \end{figure} - - \begin{description} - \item \textbf{main.py} \hfill \\ - Programme principal, il affiche une interface (en console) permettant d'éxécuter une des opérations disponibles. - \item \textbf{tests.py} \hfill \\ - Contient les corps de toutes le opérations pouvant être choisies dans le programme principal. - \item \textbf{BMPFile.py} \hfill \\ - Contient les structures \emph{BMPFile}, \emph{BMPContent}, \emph{BMPHeader}, permettant d'encoder et de décoder un fichier BMP. Ainsi que la strucure \emph{RGBPixel} permettant de coder un pixel. - \item \textbf{Noise.py} \hfill \\ - Contient tout les traitements tels que les bruitages et débruitages, les filtres, et les détections de formes et contours. (il importe tout le dossier \emph{utility}) - \item \textbf{utility/} - \begin{description} - \item \textbf{SaltAndPepper\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{Poivre \& Sel}. - \item \textbf{Additive\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{additif}. - \item \textbf{Gaussian\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{gaussien}. - \item \textbf{Multiplicative\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{multiplicatif}. - \item \textbf{Color\_Noise.py} \hfill \\ - Contient les méthodes de bruitage et débruitage du bruit de type \emph{multicolore}. - \item \textbf{Filter.py} \hfill \\ - Contient les méthodes de filtrage (standard et par convolution). - \item \textbf{Shape.py} \hfill \\ - Contient les méthodes de détection de formes et de contours - - \end{description} - \end{description} - - Le fichier \textbf{tests.py} utilise la structure \emph{BMPFile} afin de lire/écrire les fichiers ainsi que la structure \emph{Noise} qui contient toutes les méthodes de bruitage et de débruitage, les filtres et les détections de formes et de contours. Le fichier \emph{\textbf{Noise.py}} (qui contient la structure de même nom) appelle les différents fichiers contenus dans le dossier \textbf{utility}. Les fichiers contenus dans \textbf{utility} peuvent être:\\ - - Un fichier qui contient toutes les méthodes pour un type de bruit (bruitage et débruitage)\\ - - Le fichier \emph{Filters.py} qui contient toutes les méthodes de filtrage\\ - - Le fichier \emph{Shapes.py} qui contient toutes les méthodes de détection de formes et contours - - % ========================= % - % === SECT 2 - VARIABLE === % - % ========================= % - \section{Variables globales} - Ces valeurs seront utilisées pour la majeure partie des calculs et correspondent à des notations générales à l'image ou aux autres notations utilisées.\\\\ - \noindent - Soit \begin{math}(n_x, n_y)\in\N^2\end{math}, respectivement la largeur et la hauteur de l'image en pixels.\\ - Soit la matrice \begin{math}I_{n_y, n_x}\end{math} correspondant à la répartition des pixels dans l'image.\\ - Soit le couple \begin{math}(x,y)\in\N^2\end{math}, les coordonnés d'un pixel dans I, tel que \begin{math}x\in[0;n_x[,\ et\ y\in[0;n_y[\end{math}.\\ - Soit \begin{math}\vec{u}\end{math} le vecteur associé à une unité de contour, tel que \begin{math}||\vec{u}|| = 1,\ et\ sa\ direction\ d =\frac{n\pi}{2}[2\pi]\ rad,\ avec\ n\in\N\end{math}.\\ - Soit \begin{math}K_n\end{math}, la matrice carré d'ordre \emph{n}. Par exemple \begin{math}K_3 = \begin{pmatrix}k_1&k_2&k_3\\k_4&k_5&k_6\\k_7&k_8&k_9\\\end{pmatrix}\end{math}\\\\ - - - \newpage - - - - % ========================= % - % === PART 2 - ENCODAGE === % - % ========================= % - \part*{Encodage et décodage de fichiers} - - % =============================== % - % === SECT 1 - FONCTIONNEMENT === % - % =============================== % - \section{Fonctionnement global} - La lecture et l'écriture des fichiers se fait octet par octet, la structure \emph{BMPFile} permet de transformer les octets d'un fichier BMP en matrice de pixels, et inversement, de transformer une matrice de pixels en fichier BMP. Les types d'encodage des couleurs pris en charge sont 8 (nuance de gris), et 24 (rouge, vert, bleu) bits par pixels. Les pixels sont du type \emph{RGBPixel}, ce type contient les coordonnées du pixel (x, y), ainsi que sa couleur au format RGB (rouge, vert, bleu) chaque nuance prend sa valeur entre 0 et 255. - \\\par Le seul format pris en charge est le format BMP. Pour que le programme fonctionne avec d'autres formats (jpeg, png, gif, etc), il suffirait de récupérer une matrice de \emph{RGBPixel} à partir d'une image. Toutes les méthodes de traitement d'image du programme agissent uniquement sur cette matrice. Il serait ainsi possible de convertir une image vers un autre format. Le fichier BMP peut se décomposer en 3 parties: - \begin{description} - \item - Le \emph{header} donne des informations sur le fichier et son contenu. \hfill\\ - Sa taille est de 54 octets. - \item - La \emph{palette} donne des informations sur l'encodage des couleurs. \hfill\\ - Sa taille est variable, elle est donnée dans le header. - \item - Le \emph{bitmap} est la matrice de pixels, c'est le <> du fichier. \hfill\\ - Sa taille est variable, elle est donnée dans le header. - \end{description} - - - % ====================== % - % === SUBS 1 - HEADE === % - % ====================== % - \subsection{Le header} - Le header fournit des informations sur le fichier, sur l'image, sur la palette et sur les tailles et positions des différentes parties. Il est - \begin{center}\begin{tabular}{|c|c|l|} - \hline \textbf{Position (octets)} & \textbf{Taille} & \textbf{Libellé}\\ - \hline 0 & 2 octets & signature, toujours 4D42\\ - \hline 2 & 4 octets & taille du fichier (octets)\\ - \hline 6 & 2 octets & réservé (défaut: 0)\\ - \hline 8 & 2 octets & réservé (défaut: 0)\\ - \hline 10 & 4 octets & offset de la bitmap (octets)\\ - \hline 10 & 4 octets & taille du BITMAPINFOHEADER (octets)\\ - \hline 14 & 4 octets & largeur de l'image (pixels)\\ - \hline 18 & 4 octets & hauteur de l'image (pixels)\\ - \hline 22 & 2 octets & nombre de plans (défaut: 0001)\\ - \hline 26 & 2 octets & nombre de bits par pixels (1, 4, 8, ou 24)\\ - \hline 30 & 4 octets & type de compression (0=rien, 1=RLE-8, 2=RLE-4)\\ - \hline 34 & 4 octets & taille de l'image padding inclus (octets)\\ - \hline 38 & 4 octets & résolution horizontale (pixels)\\ - \hline 42 & 4 octets & résolution verticale (pixels)\\ - \hline 46 & 4 octets & nombre de couleurs ou 0\\ - \hline 50 & 4 octets & nombre de couleurs importantes ou 0\\ - - \hline - \end{tabular}\newline\newline Figure 1 - BMP Header\end{center} - - % ====================== % - % === SUBS 2 - PALET === % - % ====================== % - \subsection{La palette} - La palette permet de définir les différentes couleurs d'une image. Elle correspond à une liste de couleurs ou bien à un <> associé à un encodage spécifique. Elle dépend en premier lieu du nombre de bits par pixels. Cette valeur nous informe sur combien de bits est codée chaque couleur, les valeurs possibles sont 1, 4, 8, et 24. Le nombre de couleurs possibles dépend directement du \emph{bpp} (bits par pixels), plus il est grand, plus il y a de nuances. \begin{math}nbcouleurs = 2^{bpp}\end{math}. Les différents types de palette correspondant à un \emph{bpp} sont décrites ci-dessous. - - % ======================== % - % === S2SECT 1 - 8 BPP === % - % ======================== % - \subsubsection{Encodage en 8 bits par pixels} - Pour une image encodée en 8 bits par pixels, chaque pixel est codé sur 1 octet. Il y a donc une possibilité de \begin{math}2^8 = 256\end{math} couleurs. Ces 256 couleurs sont définies dans la \emph{palette} sur 4 octets chacunes. Les 3 premiers octets contiennent les couleurs au format RGB, et le dernier octet est à "0", c'est un octet de bourrage car la taille du fichier doit être multiple de 4. Par exemple si l'on veut que la couleur "0" corresponde à du rouge (R=255,G=0,B=0), et la couleur 1 à du rose (R=255,G=0,B=255), la palette commencera par: \\\\ - \begin{tabular}{|c|c|c|c|c|c|} - \hline - \textbf{rang}&\textbf{couleur}&\textbf{octet1}&\textbf{octet2}&\textbf{octet3}&\textbf{octet4}\\\hline - 0 & rouge & 255 & 0 & 0 & 0\\\hline - 1 & rose & 255 & 0 & 255 & 0\\\hline - .. & .. & .. & .. & .. & ..\\\hline - 255 & .. & .. & .. & .. & 0\\\hline - \end{tabular}\\\\ - Par défaut, nous utilisons uniquement le codage en 8 bits par pixels pour des nuances de gris, notre palette a donc comme valeurs:\\\\ - \begin{tabular}{|c|c|c|c|c|c|} - \hline - \textbf{rang}&\textbf{couleur}&\textbf{octetS}&\textbf{octet2}&\textbf{octet3}&\textbf{octet4}\\\hline - 0 & noir & 0 & 0 & 0 & 0\\\hline - 1 & noir & 1 & 1 & 1 & 0\\\hline - 2 & noir & 2 & 2 & 2 & 0\\\hline - .. & .. & .. & .. & .. & ..\\\hline - 254 & blanc & 254 & 254 & 254 & 0\\\hline - 255 & blanc & 255 & 255 & 255 & 0\\\hline - \end{tabular} * \begin{math}4\times256 = 1024\ octets\end{math} au total - - - % ========================= % - % === S2SECT 2 - 24 BPP === % - % ========================= % - \subsubsection{Encodage en 24 bits par pixels} - Pour une image encodée en 24 bits par pixels, chaque pixel est codé sur 3 octets au format BGR, ce format est équivalent au format RGB, mis à part l'ordre des octets qui est inversé, celà correspond à \begin{math}2^24 = 16.777.216\end{math} couleurs par pixels. La \emph{palette} associée est définie par défaut comme suit:\\\\ - \begin{tabular}{|c|c|c|c|} - \hline - \textbf{Position (octets)}&\textbf{Taille (octets)}&\textbf{Valeur entière}&\textbf{Valeur ASCII}\\\hline - 0 & 1 & 66 & "B" \\\hline - 1 & 1 & 71 & "G" \\\hline - 2 & 1 & 82 & "R" \\\hline - 3 & 1 & 115 & "s" \\\hline - 4 & 48 & 0 & inconnu\\\hline - 52 & 1 & 2 & inconnu\\\hline - 53 & 15 & 0 & inconnu \\\hline - \end{tabular} * 68 octets au total - - - % ====================== % - % === SUBS 3 - BITMA === % - % ====================== % - \subsection{Le bitmap} - - Le \emph{bitmap} du fichier (matrice de pixels) peut avoir plusieurs formats qui sont propres au nombre de bits par pixels et dépendent de la \emph{palette}. De plus la matrice est dans l'ordre inverse, c'est à dire que le premier pixel est en fait le dernier, il convient donc d'inverser les lignes et les colonnes. De manière générale les pixels sont enregistrés en une succession de lignes. Le nombre d'octets de chaque ligne doit être un multiple de 4, est donc ajouté en fin de ligne un <> composé de 0 à 3 octets ayant pour valeur zéro. - - - - - - - \newpage - - - - - - % ====================== % - % === PART 3 - BRUIT === % - % ====================== % - \part*{Bruits} - - - - - % =================== % - % === SECT1 - P&S === % - % =================== % - \section{Le bruit de type <>} - \textbf{Définition graphique: }L'image est parsemée de pixels ayant une teinte extrême (blancs ou noirs) sans rapport avec leur contexte (voisinage).\\ - \textbf{Bruitage: }Le bruitage se fait en fonction du paramètre réel \begin{math}seuil\in[0;1]\end{math}. Il y a donc \begin{math}n = seuil\times width\times height\end{math} couples \begin{math}(x, y)\end{math} aléatoires qui sont en noir ou blanc (choix aléatoire), ce qui correspond à \begin{math}(100\times seuil)\end{math} \% de la totalité des pixels.\\ - \textbf{Débruitage: }L'image est débruitée en fonction de deux paramètres \emph{seuil} et \emph{borne}, tout deux compris dans \begin{math}[0;255]\end{math}. Chaque pixel est traité si et seulement si il est proche du noir ou du blanc, c'est-à-dire compris dans \begin{math}[0;borne]\end{math} pour le noir et dans \begin{math}[255-borne;255]\end{math} pour le blanc. Chaque pixel traité est comparé à la couleur moyenne de ses 8 voisins, si la différence avec sa couleur est supérieure à \emph{seuil}, le pixel se voit attribuer la couleur moyenne de ses voisins. Il est à noter que si borne vaut 255, tout les pixels seront traités, de même que si seuil vaut 255, tout les pixels traités se verront affecter la couleur moyenne de leurs voisins.\\\\ - % effet 50% - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{lena.jpg} - Image originale - \end{minipage} - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/50.jpg} - Bruitée, seuil de 0.5 - \end{minipage} - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50.jpg} - Débruitée sans lissage - \end{minipage} - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50_liss.jpg} - Débruitée avec lissage - \end{minipage} - \end{figure} - - % effet 50% (zoom) - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{lenaZOOM.jpg} - Image originale (zoom) - \end{minipage} - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/50_ZOOM.jpg} - Bruitée, seuil de 0.5 (zoom) - \end{minipage} - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50_ZOOM.jpg} - Débruitée sans lissage (zoom) - \end{minipage} - \begin{minipage}[t]{3.5cm} - \centering - \includegraphics[width=3.5cm,height=3.5cm]{SaltAndPepper/unset_50_liss_ZOOM.jpg} - Débruitée avec lissage (zoom) - \end{minipage} - \end{figure} - On remarque bien ici que les contours subissent un <> ce qui dégrade en majeure partie le rendu. Par contre, les couleurs sont convenablement restituées, de même pour les teintes et contrastes. Il suffirait de traiter l'image en prenant en compte les contours, ce qui permettrait de gagner en qualité. - - \newpage - - - % =================== % - % === SECT2 - ADD === % - % =================== % - \section{Le bruit additif} - \textbf{Définition graphique: }L'image est parsemée de pixels ayant une teinte plus ou moins incohérente avec leurs contexte (voisinage).\\ - \textbf{Bruitage: }Le bruitage se fait en fonction du paramètre réel \begin{math}seuil\in[0;1]\end{math}. Il y a donc \begin{math}n = seuil\times width\times height\end{math} couples \begin{math}(x, y)\end{math} aléatoires qui voient leur couleur changée (choix aléatoire), ce qui correspond à \begin{math}(100\times seuil)\end{math} \% de la totalité des pixels.\\ - \textbf{Débruitage \begin{math}\textbf{n}^\textbf{o}\end{math}1: }L'image est débruitée en fonction du paramètre \begin{math}seuil \in [0;255]\end{math}. Chaque pixel traité est comparé à la couleur moyenne de ses 8 voisins, si la différence avec sa couleur est supérieure à \emph{seuil}, le pixel se voit attribuer la couleur moyenne de ses voisins. Il est à noter que si seuil vaut 255, tout les pixels traités se verront affecter la couleur moyenne de leurs voisins.\\\\ - % effet 50% - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{lena.jpg} - Image originale - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/algo1_10.jpg} - Bruitée, seuil de 0.1 - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/unset_algo1_10.jpg} - Débruitée, seuil de .35 - \end{minipage} - \end{figure} - - % effet 50% (zoom) - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{lenaZOOM.jpg} - Image originale (zoom) - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/algo1_10_ZOOM.jpg} - Bruitée, seuil de 0.1 (zoom) - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/unset_algo1_10_ZOOM.jpg} - Débruitée, seuil de .35 (zoom) - \end{minipage} - \end{figure} - On remarque bien ici qu'une majorité des pixels ajoutés lors du bruitage a été proprement retirée, mais une partie reste bien visible. Celà est du au fait que les pixels considérés comme bruit se voient appliquer un filtre moyen (couleur moyenne des voisins), mais que certains voisins peuvent être eux-aussi corrompus. De plus, certains pixels corrompus ne sont pas traités du fait qu'ils ont une teinte trop proche de la moyenne voisine. Il faut noter que lors du débruitage <> le paramètre \emph{borne} permettait d'éviter ce problème car nous avions des références absolues : le noir et le blanc. Nous avons donc décidé d'améliorer cet algorithme de débruitage (cf. \emph{Débruitage \begin{math}n^o\end{math}2}).\\\\ - - - \newpage - - - \textbf{Débruitage \begin{math}\textbf{n}^\textbf{o}\end{math}2: }L'image est débruitée en fonction du paramètre \begin{math}seuil \in \N^+ \text{, tel que } seuil \in [0;256^8]\end{math}. Pour chaque pixel, on établit la matrice carré \begin{math}M_n\end{math} d'ordre \emph{n} correspondant à son voisinage. Soit \begin{math}P_{2,2}\end{math} le pixel en cours, et - \begin{align*}M_{3} = \begin{pmatrix} - P_{1,1} & P_{1,2} & P_{1,3}\\ - P_{2,1} & P_{2,2} & P_{2,3}\\ - P_{3,1} & P_{3,2} & P_{3,3}\\ - \end{pmatrix}\end{align*} - - \noindent On calcule ensuite \begin{math}M'_n\end{math} la matrice carré d'ordre \emph{n} correspondant à la différence absolue à \begin{math}P_{2,2}\end{math}. - \begin{align*} - M'_3 = - \begin{pmatrix} - |P_{1,1}-P_{2,2}| & |P_{1,2}-P_{2,2}| & |P_{1,3}-P_{2,2}|\\ - |P_{2,1}-P_{2,2}| & |P_{2,2}-P_{2,2}| & |P_{2,3}-P_{2,2}|\\ - |P_{3,1}-P_{2,2}| & |P_{3,2}-P_{2,2}| & |P_{3,3}-P_{2,2}|\\ - \end{pmatrix} - = - \begin{pmatrix} - |P_{1,1}-P_{2,2}| & |P_{1,2}-P_{2,2}| & |P_{1,3}-P_{2,2}|\\ - |P_{2,1}-P_{2,2}| & 0 & |P_{2,3}-P_{2,2}|\\ - |P_{3,1}-P_{2,2}| & |P_{3,2}-P_{2,2}| & |P_{3,3}-P_{2,2}|\\ - \end{pmatrix} - \end{align*} - \noindent Soit \emph{m} les valeurs de \begin{math}M'_n\end{math} classées par ordre décroissant, ce qui nous permet de calculer le \textbf{poids statistique} du pixel. - \begin{align*} - PS(P_{2,2}) = \sum_{i=0}^{\frac{n^2}{2}}{\ m_i} - \end{align*} - Si le poids statistique d'un pixel est supérieur à \emph{seuil}, alors un filtre moyen lui est appliqué. - - - % effet 50% - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{lena.jpg} - Image originale - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/algo1_10.jpg} - Bruitée, seuil de 0.1 - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/unset_algo1_10.jpg} - Débruitée, seuil de .35 - \end{minipage} - \end{figure} - - % effet 50% (zoom) - \begin{figure}[!ht] - \centering - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{lenaZOOM.jpg} - Image originale (zoom) - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/algo1_10_ZOOM.jpg} - Bruitée, seuil de 0.1 (zoom) - \end{minipage} - \begin{minipage}[t]{5cm} - \centering - \includegraphics[width=5cm,height=5cm]{Additive/unset_algo1_10_ZOOM.jpg} - Débruitée, seuil de .35 (zoom) - \end{minipage} - \end{figure} - On remarque bien ici qu'une majorité des pixels ajoutés lors du bruitage a été proprement retirée, mais une partie reste bien visible. Celà est du au fait que les pixels considérés comme bruit se voient appliquer un filtre moyen (couleur moyenne des voisins), mais que certains voisins peuvent être eux-aussi corrompus. De plus, certains pixels corrompus ne sont pas traités du fait qu'ils ont une teinte trop proche de la moyenne voisine. Il faut noter que lors du débruitage <> le paramètre \emph{borne} permettait d'éviter ce problème car nous avions des références absolues : le noir et le blanc. Nous avons donc décidé d'améliorer cet algorithme de débruitage (cf. \emph{Débruitage \begin{math}n^o\end{math}2}). - - -\end{document} \ No newline at end of file diff --git a/dossier/dossier.pdf b/dossier/dossier.pdf index 5a93ca669d5183d392aedbf378071d5e7669264e..f18c782d6558e653594e5d7d77cd6786613a991d 100644 GIT binary patch delta 113555 zcmV(@K-Ry%?NZgpRe*#6gaU*Egam{Iga(8Mgb0KQgbK6^W?BR?F)%Qf@I3)2f2A1P zZsW%F-Cx0%VksbE=00SCylgftwg`Gb_bpBVTB2d_vz1cW^yg}` z)!wFe-B=I2Ecg3*sP8wwe)=n5fAUF@6;3%sB$BO?S*|4G_<$fuo>}9Qt;nSGynic{ zrX!IlT_m%U>Q+c|1nli`vo%GYK6K^jvHm<(-|=(O&YqLPWkMNvmM;JNu0E6xK(Em{ zt@;5MCOws1UmZ3o=Vy&qX%%4h;rCQOS5430B|?6|f8`PX2Q1?;zz$W-fBRKH-;6z< zv?K2vT7Zygz*ir4?c?(Gu9{)c{NmF_`Lx>cPE(CtPXYoRm@Jq0n-$hFpSw2Pe?}A< zF5C($2t!-v$OFuSD))!`?t)KuUTT6!|ql{a*rIX!9QIVj`Hn&aC{yO{*6WW;5&w4ZWl z>n^_fd8~O6Jf%WM!R5f)=ykjTVrL(S^07QVT!oOx3PZH@^O*=AA$24IiTnltluUQk z1wp&E11e9Xr1rSSf6bh|!wNxG7haM=tSC@>J=4yM3Uo%USk^+W5WJn#*K++6|LiL! 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Pour chaque pixel, on établit la matrice carré \begin{math}M_n\end{math} d'ordre \emph{n} correspondant à son voisinage. Soit \begin{math}P_{2,2}\end{math} le pixel en cours, et + \textbf{Débruitage \begin{math}\textbf{n}^\textbf{o}\end{math}2: }L'image est débruitée en fonction du paramètre \begin{math}seuil \in \N^+ \text{, tel que } seuil \in [0;8\times256]\end{math}. Pour chaque pixel, on établit la matrice carré \begin{math}M_n\end{math} d'ordre \emph{n} correspondant à son voisinage. Soit \begin{math}P_{2,2}\end{math} le pixel en cours, et \begin{align*}M_{3} = \begin{pmatrix} P_{1,1} & P_{1,2} & P_{1,3}\\ P_{2,1} & P_{2,2} & P_{2,3}\\ @@ -382,13 +382,13 @@ = \begin{pmatrix} |P_{1,1}-P_{2,2}| & |P_{1,2}-P_{2,2}| & |P_{1,3}-P_{2,2}|\\ - |P_{2,1}-P_{2,2}| & 0 & |P_{2,3}-P_{2,2}|\\ + |P_{2,1}-P_{2,2}| & 0 & |P_{2,3}-P_{2,2}|\\ |P_{3,1}-P_{2,2}| & |P_{3,2}-P_{2,2}| & |P_{3,3}-P_{2,2}|\\ \end{pmatrix} \end{align*} \noindent Soit \emph{m} les valeurs de \begin{math}M'_n\end{math} classées par ordre décroissant, ce qui nous permet de calculer le \textbf{poids statistique} du pixel. \begin{align*} - PS(P_{2,2}) = \sum_{i=0}^{\frac{n^2}{2}}{\ m_i} + PS(P_{2,2}) = \sum_{i=0}^{\frac{n^2-1}{2}}{\ m_i} \end{align*} Si le poids statistique d'un pixel est supérieur à \emph{seuil}, alors un filtre moyen lui est appliqué.

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