Neste trabalho são apresentadas, complementadas e melhoradas duas técnicas de restauração de imagens: uma abordando o problema de retoque digital/remoção de objetos enquanto a segunda é direcionada ao problema deneliminação de ruído. Em ambas as técnicas, a ideia é trabalhar com imagens contendo texturas e outras características de interesse para um observador humano como a preservação de padrões, bordas, estruturas e regiões de natureza oscilatória. A técnica descrita sobre retoque digital de imagens combina difusão anisotrópica, síntese de texturas, busca dinâmica e um novo termo empregado no mecanismo de atribuição da ordem de prioridade durante o processo de reconstrução. Assim, dada uma imagem com regiões a serem recompostas, uma técnica de difusão anisotrópica é aplicada à imagem afim de se obter um mapa de saliência contendo bordas, estruturas e demais informações de baixa frequência da imagem. Na sequência, um mecanismo de prioridade baseado em um novo termo de confiabilidade regularizado é calculado a partir da combinação do mapa anteriormente gerado com a equação do transporte. Tal mecanismo é utilizado para determinar a ordem de preenchimento das partes faltantes da imagem. Para essa tarefa, a abordagem apresentada utiliza uma nova medida de similaridade entre blocos de pixels(amostrados dinamicamente para acelerar o processo), afim de encontrar os melhores candidatos a serem alocados nas regiões danificadas. A técnica destinada à remoção de ruídos alia a teoria da difusão anisotrópica, técnicas de análise harmônica e modelos numéricos de discretização de EDPs não-lineares em uma equação diferencial parcial regularizada, a qual atua de forma incisiva em regiões mais homogêneas da imagem e de forma mais suave em regiões caracterizadas como textura e bordas, preservando, assim, essas regiões. Além da natureza anisotrópica, a EDP procura recompor partes texturizadas perdidas no processo de eliminação de ruído através da aplicação de técnicas robustas de análise harmônica. Uma validação teórica e experimental para esta EDP e um estudo do ajuste paramétrico do método de eliminação de ruído baseado nesta EDP foram realizados neste trabalho. A eficiência e a performance das técnicas propostas são atestadas por meio das análises experimentais quantitativas e qualitativas com outras abordagens clássicas da literatura. / In this work two techniques of image restoration are presented, complemented and improved: one approaching the problem of image inpainting/object removal problem while the second one dealing with the image denoising problem. In both cases, the core idea is to process images containing textures and other features perceptible to a human observer such as patterns, contours, structures and oscillatory information. The image inpainting technique combines anisotropic diffusion, texture synthesis, dynamic search and a mechanism to set the order of priority during the image completion process. More precisely, given an image and target region to be inpainted, an anisotropic diffusion technique is applied in order to generate a saliency map containing edges, structures and other low frequency parts of the image. Next, apriority mechanism based on a new biased confidence term is computed from the map previously generated with the transport equation to define the level of priority of the pixels during the filling procedure. To accomplish this task, the presented approach employs a novel measure of similarity wich measures the distance between blocks of pixels (sampled dynamically to speed up the process) in order to find the best candidates to be allocated in the damaged regions. The technique devoted to denoising an image combines the theory of anisotropic diffusion, harmonic analysis techniques and numerical models into a regularized partial differential equation, which diffuses the pixels more incisively on homogeneous regions of the image while still seeking to attenuate regions formed by textures and patterns, thus preserving those information. Moreover, the proposed PDE aims at recovering texturized regions which have been degraded during the denoising process by employing harmonic analysis tools. A theoretical and experimental validation for this EDP and a study of the parametric adjustment of the image denoising method based on this EDP were performed in this work. The effectivenss and performance of the proposed approaches are attested through a comprehensive set of comparisons against other representative techniques in the literature.

De nos jours, l'augmentation rapide de les vidéos crée une demande massive d'applications d'édition de vidéos. Dans cette thèse, nous résolvons plusieurs problèmes relatifs au post-traitement vidéo. Nous nous concentrons sur l'application de suppression d'objets en vidéo. Pour mener à bien cette tâche, nous l'avons divisé en deux problèmes: (1) une étape de segmentation des objets vidéo pour sélectionner les objets à supprimer et (2) une étape d'inpainting vidéo pour remplir les zones endommagées. Pour le problème de la segmentation vidéo, nous concevons un système adapté aux applications de suppression d’objets avec différentes exigences en termes de précision et d’efficacité. Notre approche repose sur la combinaison de réseaux de neurones convolutifs (CNN) pour la segmentation et de la méthode classique de suivi des masks. Nous adoptons des réseaux de segmentation d’images et les appliquons à la casse vidéo en effectuant une segmentation image par image. En exploitant à la fois les formations en ligne et hors ligne avec uniquement une annotation de première image, les réseaux sont en mesure de produire une segmentation extrêmement précise des objets vidéo. En outre, nous proposons un module de suivi de masque pour assurer la continuité temporelle et un module de liaison de masque pour assurer la cohérence de l'identité entre les trames. De plus, nous présentons un moyen simple d’apprendre la couche de dilatation dans le masque, ce qui nous aide à créer des masques appropriés pour l’application de suppression d’objets vidéo.Pour le problème d’inpainting vidéo, nous divisons notre travail en deux catégories basées sur le type de fond. En particulier, nous présentons une méthode simple de propagation de pixels guidée par le mouvement pour traiter les cas d’arrière-plan statiques. Nous montrons que le problème de la suppression d'objets avec un arrière-plan statique peut être résolu efficacement en utilisant une technique simple basée sur le mouvement. Pour traiter le fond dynamique, nous introduisons la méthode d’inpainting vidéo en optimisant une fonction d’énergie globale basée sur des patchs. Pour augmenter la vitesse de l'algorithme, nous avons proposé une extension parallèle de l'algorithme 3D PatchMatch. Pour améliorer la précision, nous intégrons systématiquement le flux optique dans le processus global. Nous nous retrouvons avec une méthode d’inpainting vidéo capable de reconstruire des objets en mouvement ainsi que de reproduire des textures dynamiques tout en fonctionnant dans des délais raisonnables.Enfin, nous combinons les méthodes de segmentation des objets vidéo et d’inpainting vidéo dans un système unifié pour supprimer les objets non souhaités dans les vidéos. A notre connaissance, il s'agit du premier système de ce type. Dans notre système, l'utilisateur n'a qu'à délimiter approximativement dans le premier cadre les objets à modifier. Ce processus d'annotation est facilité par l'aide de superpixels. Ensuite, ces annotations sont affinées et propagées dans la vidéo par la méthode de segmentation des objets vidéo. Un ou plusieurs objets peuvent ensuite être supprimés automatiquement à l’aide de nos méthodes d’inpainting vidéo. Il en résulte un outil de montage vidéo informatique flexible, avec de nombreuses applications potentielles, allant de la suppression de la foule à la correction de scènes non physiques. / Nowadays, the rapid increase of video creates a massive demand for video-based editing applications. In this dissertation, we solve several problems relating to video post-processing and focus on objects removal application in video. To complete this task, we divided it into two problems: (1) A video objects segmentation step to select which objects to remove and (2) a video inpainting step to filling the damaged regions.For the video segmentation problem, we design a system which is suitable for object removal applications with different requirements in terms of accuracy and efficiency. Our approach relies on the combination of Convolutional Neural Networks (CNNs) for segmentation and the classical mask tracking method. In particular, we adopt the segmentation networks for image case and apply them to video case by performing frame-by-frame segmentation. By exploiting both offline and online training with first frame annotation only, the networks are able to produce highly accurate video object segmentation. Besides, we propose a mask tracking module to ensure temporal continuity and a mask linking module to ensure the identity coherence across frames. Moreover, we introduce a simple way to learn the dilation layer in the mask, which helps us create suitable masks for video objects removal application.For the video inpainting problem, we divide our work into two categories base on the type of background. In particular, we present a simple motion-guided pixel propagation method to deal with static background cases. We show that the problem of objects removal with a static background can be solved efficiently using a simple motion-based technique. To deal with dynamic background, we introduce video inpainting method by optimization a global patch-based energy function. To increase the speed of the algorithm, we proposed a parallel extension of the 3D PatchMatch algorithm. To improve accuracy, we systematically incorporate the optical flow in the overall process. We end up with a video inpainting method which is able to reconstruct moving objects as well as reproduce dynamic textures while running in a reasonable time.Finally, we combine the video objects segmentation and video inpainting methods into a unified system to removes undesired objects in videos. To the best of our knowledge, this is the first system of this kind. In our system, the user only needs to approximately delimit in the first frame the objects to be edited. These annotation process is facilitated by the help of superpixels. Then, these annotations are refined and propagated through the video by the video objects segmentation method. One or several objects can then be removed automatically using our video inpainting methods. This results in a flexible computational video editing tool, with numerous potential applications, ranging from crowd suppression to unphysical scenes correction.

The thesis deals with addition of pixels outside the image. Lists some methods for inpainting using computers and highlights the pitfalls that appear here. Examines methods for interpolation and approximation of functions in order to find the best method for extrapolating the image beyond its borders. Describes the basics of Wavelet transformation and Multiresolution analysis. It is proposed several methods for replenishment of pixels outside the image. PSNR and SSIM are used to compare achieved results. These methods are explained and compared. Briefly discusses the algorithm OMP, falling within the sparse representation of signals, and used in one of the methods. Also discussed is the development environment of MATLAB as a tool for the implementation of algorithms that practically solves the given problem. The practical part describes the implemented methods for adding pixels outside the image.

The thesis deals with addition of pixels outside the image. Lists some methods for inpainting using computers and highlights the pitfalls that appear here. Examines methods for interpolation and approximation of functions in order to find the best method for extrapolating the image beyond its borders. Describes the basics of Wavelet transformation and Multiresolution analysis and briefly discusses about spatial filtering, edge detection and the algorithm OMP, falling within the sparse representation of signals. Theoretical knowledge of these areas are used in the design of several methods for adding pixels outside the image. PSNR and SSIM are used to compare achieved results. Also discussed is the development environment of MATLAB as a tool for the implementation of algorithms that practically solves the given problem.

V oblasti zpracování signálů se v současné době čím dál více využívají tzv. řídké reprezentace signálů, tzn. že daný signál je možné vyjádřit přesně či velmi dobře aproximovat lineární kombinací velmi malého počtu vektorů ze zvoleného reprezentačního systému. Tato práce se zabývá využitím řídkých reprezentací pro rekonstrukci poškozených zvukových záznamů, ať už historických nebo nově vzniklých. Především historické zvukové nahrávky trpí zarušením jako praskání nebo šum. Krátkodobé poškození zvukových nahrávek bylo doposud řešeno interpolačními technikami, zejména pomocí autoregresního modelování. V nedávné době byl představen algoritmus s názvem Audio Inpainting, který řeší doplňování chybějících vzorků ve zvukovém signálu pomocí řídkých reprezentací. Zmíněný algoritmus využívá tzv. hladové algoritmy pro řešení optimalizačních úloh. Cílem této práce je porovnání dosavadních interpolačních metod s technikou Audio Inpaintingu. Navíc, k řešení optimalizačních úloh jsou využívány algoritmy založené na l1-relaxaci, a to jak ve formě analyzujícího, tak i syntetizujícího modelu. Především se jedná o proximální algoritmy. Tyto algoritmy pracují jak s jednotlivými koeficienty samostatně, tak s koeficienty v závislosti na jejich okolí, tzv. strukturovaná řídkost. Strukturovaná řídkost je dále využita taky pro odšumování zvukových nahrávek. Jednotlivé algoritmy jsou v praktické části zhodnoceny z hlediska nastavení parametrů pro optimální poměr rekonstrukce vs. výpočetní čas. Všechny algoritmy popsané v práci jsou na praktických příkladech porovnány pomocí objektivních metod odstupu signálu od šumu (SNR) a PEMO-Q. Na závěr je úspěšnost rekonstrukce poškozených zvukových signálů vyhodnocena.

This document elaborates on the subject of video interlacing and its removal. It describes the interlacing of video, its history and the reasons that led to its use. The document also explains why it is necessary to remove interlacing and the basic methods that are used for it. It describes the proposed deinterlacing algorithm and its implementation, including description of inpainting and block matching. Included are also test results of both quality and speed of my deinterlacing algorithm. The final chapter describes the implementation as a console application and a DLL library.

Three Dimensional Video (3DV) has become increasingly popular with the success of 3D cinema. Moreover, emerging display technology offers an immersive experience to the viewer without the necessity of any visual aids such as 3D glasses. 3DV applications, Three Dimensional Television (3DTV) and Free Viewpoint Television (FTV) are auspicious technologies for living room environments by providing immersive experience and look around facilities. In order to provide such an experience, these technologies require a number of camera views captured from different viewpoints. However, the capture and transmission of the required number of views is not a feasible solution, and thus view rendering is employed as an efficient solution to produce the necessary number of views. Depth-image-based rendering (DIBR) is a commonly used rendering method. Although DIBR is a simple approach that can produce the desired number of views, inherent artifacts are major issues in the view rendering. Despite much effort to tackle the rendering artifacts over the years, rendered views still contain visible artifacts. This dissertation addresses three problems in order to improve 3DV quality: 1) How to improve the rendered view quality using a direct approach without dealing each artifact specifically. 2) How to handle disocclusions (a.k.a. holes) in the rendered views in a visually plausible manner using inpainting. 3) How to reduce spatial inconsistencies in the rendered view. The first problem is tackled by an edge-aided rendering method that uses a direct approach with one-dimensional interpolation, which is applicable when the virtual camera distance is small. The second problem is addressed by using a depth-based inpainting method in the virtual view, which reconstructs the missing texture with background data at the disocclusions. The third problem is undertaken by a rendering method that firstly inpaint occlusions as a layered depth image (LDI) in the original view, and then renders a spatially consistent virtual view. Objective assessments of proposed methods show improvements over the state-of-the-art rendering methods. Visual inspection shows slight improvements for intermediate views rendered from multiview videos-plus-depth, and the proposed methods outperforms other view rendering methods in the case of rendering from single view video-plus-depth. Results confirm that the proposed methods are capable of reducing rendering artifacts and producing spatially consistent virtual views. In conclusion, the view rendering methods proposed in this dissertation can support the production of high quality virtual views based on a limited number of input views. When used to create a multi-scopic presentation, the outcome of this dissertation can benefit 3DV technologies to improve the immersive experience.

Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the resulting partitions against state-of-the-art methods as well as through a variety of quantitative measures typically employed as benchmark by the image segmentation community. Our spectral-based segmentation algorithm combines image decomposition, similarity metrics, and spectral graph theory into a concise and powerful framework. An image decomposition is performed to split the input image into texture and cartoon components. Then, an affinity graph is generated and weights are assigned to the edges of the graph according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. Moreover, the image partitioning can be improved by changing the graph weights by sketching interactively. Visual and numerical evaluation were conducted against representative spectral-based segmentation techniques using boundary and partition quality measures in the well-known BSDS dataset. Unlike most existing seed-based methods that rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima, our segmentation approach is mathematically simple to formulate, easy-to-implement, and it guarantees to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are preserved closer to each other while big discontinuities are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed approach significantly outperforms competing techniques both quantitatively as well as qualitatively, using the classical GrabCut dataset from Microsoft as a benchmark. While most of this research concentrates on the particular problem of segmenting an image, we also develop two new techniques to address the problem of image inpainting and photo colorization. Both methods couple the developed segmentation tools with other computer vision approaches in order to operate properly. / Segmentar uma image é visto nos dias de hoje como uma prerrogativa para melhorar a capacidade de sistemas de computador para realizar tarefas complexas de natureza cognitiva tais como detecção de objetos, reconhecimento de padrões e monitoramento de alvos. Esta pesquisa de doutorado visa estudar dois temas de fundamental importância no contexto de segmentação de imagens: clusterização espectral e segmentação interativa de imagens. Foram propostos dois novos algoritmos de segmentação dentro das linhas supracitadas, os quais se baseiam em operadores do Laplaciano, teoria espectral de grafos e na minimização de funcionais de energia. A eficácia de ambos os algoritmos pode ser constatada através de avaliações visuais das segmentações originadas, como também através de medidas quantitativas computadas com base nos resultados obtidos por técnicas do estado-da-arte em segmentação de imagens. Nosso primeiro algoritmo de segmentação, o qual ´e baseado na teoria espectral de grafos, combina técnicas de decomposição de imagens e medidas de similaridade em grafos em uma única e robusta ferramenta computacional. Primeiramente, um método de decomposição de imagens é aplicado para dividir a imagem alvo em duas componentes: textura e cartoon. Em seguida, um grafo de afinidade é gerado e pesos são atribuídos às suas arestas de acordo com uma função escalar proveniente de um operador de produto interno. Com base no grafo de afinidade, a imagem é então subdividida por meio do processo de corte espectral. Além disso, o resultado da segmentação pode ser refinado de forma interativa, mudando-se, desta forma, os pesos do grafo base. Experimentos visuais e numéricos foram conduzidos tomando-se por base métodos representativos do estado-da-arte e a clássica base de dados BSDS a fim de averiguar a eficiência da metodologia proposta. Ao contrário de grande parte dos métodos existentes de segmentação interativa, os quais são modelados por formulações matemáticas complexas que normalmente não garantem solução única para o problema de segmentação, nossa segunda metodologia aqui proposta é matematicamente simples de ser interpretada, fácil de implementar e ainda garante unicidade de solução. Além disso, o método proposto possui um comportamento anisotrópico, ou seja, pixels semelhantes são preservados mais próximos uns dos outros enquanto descontinuidades bruscas são impostas entre regiões da imagem onde as bordas são mais salientes. Como no caso anterior, foram realizadas diversas avaliações qualitativas e quantitativas envolvendo nossa técnica e métodos do estado-da-arte, tomando-se como referência a base de dados GrabCut da Microsoft. Enquanto a maior parte desta pesquisa de doutorado concentra-se no problema específico de segmentar imagens, como conteúdo complementar de pesquisa foram propostas duas novas técnicas para tratar o problema de retoque digital e colorização de imagens.

Submitted by Cleide Dantas (cleidedantas@ufpa.br) on 2014-07-31T17:07:14Z No. of bitstreams: 2 license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Dissertacao_AbordagemHibridaSemiautomatica.pdf: 8888210 bytes, checksum: 7cb1877477066b6d6665b67a12a9ff86 (MD5) / Approved for entry into archive by Ana Rosa Silva (arosa@ufpa.br) on 2014-09-05T13:09:45Z (GMT) No. of bitstreams: 2 license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Dissertacao_AbordagemHibridaSemiautomatica.pdf: 8888210 bytes, checksum: 7cb1877477066b6d6665b67a12a9ff86 (MD5) / Made available in DSpace on 2014-09-05T13:09:45Z (GMT). No. of bitstreams: 2 license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Dissertacao_AbordagemHibridaSemiautomatica.pdf: 8888210 bytes, checksum: 7cb1877477066b6d6665b67a12a9ff86 (MD5) Previous issue date: 2014 / Os principais objetivos deste trabalho são propor um algoritmo eficiente e o mais automático possível para estimar o que está coberto por regiões de nuvens e sombras em imagens de satélite; e um índice de confiabilidade, que seja aplicado previamente à imagem, visando medir a viabilidade da estimação das regiões cobertas pelos componentes atmosféricos usando tal algoritmo. A motivação vem dos problemas causados por esses elementos, entre eles: dificultam a identificação de objetos de imagem, prejudicam o monitoramento urbano e ambiental, e desfavorecem etapas cruciais do processamento digital de imagens para extrair informações ao usuário, como segmentação e classificação. Através de uma abordagem híbrida, é proposto um método para decompor regiões usando um filtro passa-baixas não-linear de mediana, a fim de mapear as regiões de estrutura (homogêneas), como vegetação, e de textura (heterogêneas), como áreas urbanas, na imagem. Nessas áreas, foram aplicados os métodos de restauração Inpainting por suavização baseado em Transformada Cosseno Discreta (DCT), e Síntese de Textura baseada em modelos, respectivamente. É importante salientar que as técnicas foram modificadas para serem capazes de trabalhar com imagens de características peculiares que são obtidas por meio de sensores de satélite, como por exemplo, as grandes dimensões e a alta variação espectral. Já o índice de confiabilidade, tem como objetivo analisar a imagem que contém as interferências atmosféricas e daí estimar o quão confiável será a redefinição com base no percentual de cobertura de nuvens sobre as regiões de textura e estrutura. Tal índice é composto pela combinação do resultado de algoritmos supervisionados e não-supervisionados envolvendo 3 métricas: Exatidão Global Média (EGM), Medida De Similaridade Estrutural (SSIM) e Confiança Média Dos Pixels (CM). Finalmente, verificou-se a eficácia destas metodologias através de uma avaliação quantitativa (proporcionada pelo índice) e qualitativa (pelas imagens resultantes do processamento), mostrando ser possível a aplicação das técnicas para solucionar os problemas que motivaram a realização deste trabalho. / The main goals of this work are to propose a more automatic and efficient algorithm to replace regions of clouds and shadows in satellite images as well as an index of reliability that is previously applied to each image, in order to measure the feasibility of the estimation of the regions covered by atmospheric components using that algorithm. The motivation comes from the problems caused by these atmospheric elements, among them: to impede the identification of objects of the image, to make the urban and environmental monitoring more difficult, and to interfere in crucial stages of digital image processing to extract information for the user, such as segmentation and classification. Through a hybrid approach is proposed a method for decomposing regions using a median non-linear low-pass filter, in order to map the regions of structure (homogeneous) and texture (heterogeneous) in the image. In these areas was applied restoration methods Inpainting by Smoothing based on Discrete Cosine Transform (DCT), and Exemplar-Based Texture Synthesis, respectively. It's important to note that the techniques have been modified to be able to work with images obtained through of satellite sensors with peculiar features such as large size and/or high spectral variation. Regarding to the reliability index, it aims to analyze the image that contains atmospheric interference and hence estimate how much reliable will be the redefinition, based on the percentage of cloud cover over the regions of texture and structure. This index is composed by combining the result of supervised and unsupervised algorithms involving three metrics: Average of Accuracy Global, Measure Of Structural Similarity (SSIM) and Average of Pixels Confidence. Finally, it was verified the effectiveness of these methods through a quantitative assessment (provided by the index) and qualitative (the images resulting from processing), showing the possible application of the techniques to solve the problems that motivated this work.

Les satellites de télédétection sont devenus incontournables pour la société civile. En effet, les images satellites ont été exploitées avec succès pour traiter plusieurs applications, notamment la surveillance de l'environnement et de la prévention des catastrophes naturelles. Dans les dernières années, l'augmentation de la disponibilité de très haute résolution spatiale (THR) d'images de télédétection abouti à de nouvelles applications potentiellement pertinentes liées au suivi d'utilisation des sols et à la gestion environnementale. Cependant, les capteurs optiques, en raison du fait qu'ils acquièrent directement la lumière réfléchie par le soleil, ils peuvent souffrir de la présence de nuages dans le ciel et / ou d'ombres sur la terre. Il s'agit du problème des données manquantes, qui induit un problème important et crucial, en particulier dans le cas des images THR, où l'augmentation des détails géométriques induit une grande perte d'informations. Dans cette thèse, de nouvelles méthodologies de détection et de reconstruction de la région contenant des données manquantes dans les images THR sont proposées et appliquées sur les zones contaminées par la présence de nuages et / ou d'ombres. En particulier, les contributions méthodologiques proposées comprennent: i) une stratégie multirésolution d'inpainting visant à reconstruire les images contaminées par des nuages ; ii) une nouvelle combinaison d'information radiométrique et des informations de position spatiale dans deux noyaux spécifiques pour effectuer une meilleure reconstitution des régions contaminés par les nuages en adoptant une régression par méthode a vecteurs supports (RMVS) ; iii) l'exploitation de la théorie de l'échantillonnage compressé avec trois stratégies différentes (orthogonal matching pursuit, basis pursuit et une solution d'échantillonnage compressé, basé sur un algorithme génétique) pour la reconstruction d'images contaminés par des nuages; iv) une chaîne de traitement complète qui utilise une méthode à vecteurs de supports (SVM) pour la classification et la détection des zones d'ombre, puis une régression linéaire pour la reconstruction de ces zones, et enfin v) plusieurs critères d'évaluation promptes à évaluer la performance de reconstruction des zones d'ombre. Toutes ces méthodes ont été spécialement développées pour fonctionner avec des images très haute résolution. Les résultats expérimentaux menés sur des données réelles sont présentés afin de montrer et de confirmer la validité de toutes les méthodes proposées. Ils suggèrent que, malgré la complexité des problèmes, il est possible de récupérer de façon acceptable les zones manquantes masquées par les nuages ou rendues erronées les ombres.