This work deals with the task of image scaling using GPU paralelization. Portion of text is devoted to signal processing and his affection of whole result including measuring it's quality. Describtion of the most important methods including super-resolution is given further in the text. An important part of this thesis is library implementing choosen methods with usage of paralelization on graphic chip. Achieved results of paralelization are demonstrated on set of speed tests.

No description available.

This thesis outlines a multiple-camera wireless image superresolution system which uses off-the-shelf components. The system presented demonstrates the reconstruction of a high-resolution image from multiple low-resolution images acquired from different wireless camera nodes. Each camera node participating in the system consists of a dedicated camera for image acquisition as well as a Bluetooth USB communications card for wireless transmission of data. Low-resolution images are captured at these nodes and are transmitted to the central vision server, where they are processed and registered onto a common projective plane. The registration process is arrived at through the RANdom SAmple Consensus (RANSAC) algorithm. Once the set of low-resolution images has been registered, a single high-resolution image is reconstructed. The super-resolution process used to obtain the high-resolution output is the Projection Onto Convex Sets (POCS) technique. Reconstruction results are presented. / Thesis / Master of Applied Science (MASc)

Avec le besoin croissant en bande-passante, les technologies dites de radio-cognitive sont de plus en plus étudiées par la communauté scientifique. L'enjeu est d'utiliser au mieux le spectre disponible. L'une de ces technologies, Transform Domain Communication System (TDCS), dont les performances en termes d'efficacité énergétique et spectrale étaient jusqu'à présent méconnues, constitue le sujet d'étude de cette thèse. Après une présentation du contexte scientifique et industriel de la thèse, le système TDCS est introduit, ainsi que ses similarités et différences avec OFDM et MC-CDMA. Le système est ensuite décrit sous le formalisme algébrique des modulations linaires. Cela a permis d'établir une expression de l'efficacité spectrale du système. Plusieurs techniques sont alors proposées pour améliorer celle-ci tout en améliorant, dans certains cas, le taux d'erreur binaire. Étant composé d'un de plusieurs composantes sinusoïdales, le signal TDCS souffre d'un fort Peak-to-Average Power Ratio (PAPR). La théorie ensembliste est alors présentée puis mise à profit en troisième partie de cette thèse pour proposer les algorithmes Douglas- Rachford et ROCS de réduction du PAPR des signaux TDCS. Ces algorithmes convergent plus rapidement et vers des valeurs plus basses que l'algorithme POCS précédemment utilisé dans la littérature.

This thesis examines the concept of image self-similarity and provides solutions to various associated inverse problems such as resolution enhancement and missing fractal codes. In general, many real-world inverse problems are ill-posed, mainly because of the lack of existence of a unique solution. The procedure of providing acceptable unique solutions to such problems is known as regularization. The concept of image prior, which has been of crucial importance in image modelling and processing, has also been important in solving inverse problems since it algebraically translates to the regularization procedure. Indeed, much recent progress in imaging has been due to advances in the formulation and practice of regularization. This, coupled with progress in optimization and numerical analysis, has yielded much improvement in computational methods of solving inverse imaging problems. Historically, the idea of self-similarity was important in the development of fractal image coding. Here we show that the self-similarity properties of natural images may be used to construct image priors for the purpose of addressing certain inverse problems. Indeed, new trends in the area of non-local image processing have provided a rejuvenated appreciation of image self-similarity and opportunities to explore novel self-similarity-based priors. We first revisit the concept of fractal-based methods and address some open theoretical problems in the area. This includes formulating a necessary and sufficient condition for the contractivity of the block fractal transform operator. We shall also provide some more generalized formulations of fractal-based self-similarity constraints of an image. These formulations can be developed algebraically and also in terms of the set-based method of Projection Onto Convex Sets (POCS). We then revisit the traditional inverse problems of single frame image zooming and multi-frame resolution enhancement, also known as super-resolution. Some ideas will be borrowed from newly developed non-local denoising algorithms in order to formulate self-similarity priors. Understanding the role of scale and choice of examples/samples is also important in these proposed models. For this purpose, we perform an extensive series of numerical experiments and analyze the results. These ideas naturally lead to the method of self-examples, which relies on the regularity properties of natural images at different scales, as a means of solving the single-frame image zooming problem. Furthermore, we propose and investigate a multi-frame super-resolution counterpart which does not require explicit motion estimation among video sequences.

This thesis examines the concept of image self-similarity and provides solutions to various associated inverse problems such as resolution enhancement and missing fractal codes. In general, many real-world inverse problems are ill-posed, mainly because of the lack of existence of a unique solution. The procedure of providing acceptable unique solutions to such problems is known as regularization. The concept of image prior, which has been of crucial importance in image modelling and processing, has also been important in solving inverse problems since it algebraically translates to the regularization procedure. Indeed, much recent progress in imaging has been due to advances in the formulation and practice of regularization. This, coupled with progress in optimization and numerical analysis, has yielded much improvement in computational methods of solving inverse imaging problems. Historically, the idea of self-similarity was important in the development of fractal image coding. Here we show that the self-similarity properties of natural images may be used to construct image priors for the purpose of addressing certain inverse problems. Indeed, new trends in the area of non-local image processing have provided a rejuvenated appreciation of image self-similarity and opportunities to explore novel self-similarity-based priors. We first revisit the concept of fractal-based methods and address some open theoretical problems in the area. This includes formulating a necessary and sufficient condition for the contractivity of the block fractal transform operator. We shall also provide some more generalized formulations of fractal-based self-similarity constraints of an image. These formulations can be developed algebraically and also in terms of the set-based method of Projection Onto Convex Sets (POCS). We then revisit the traditional inverse problems of single frame image zooming and multi-frame resolution enhancement, also known as super-resolution. Some ideas will be borrowed from newly developed non-local denoising algorithms in order to formulate self-similarity priors. Understanding the role of scale and choice of examples/samples is also important in these proposed models. For this purpose, we perform an extensive series of numerical experiments and analyze the results. These ideas naturally lead to the method of self-examples, which relies on the regularity properties of natural images at different scales, as a means of solving the single-frame image zooming problem. Furthermore, we propose and investigate a multi-frame super-resolution counterpart which does not require explicit motion estimation among video sequences.

Ce mémoire porte sur l'agrandissement des images numériques fixes en niveaux de gris dans un contexte général sans connaissance a priori. Il est constitué de trois parties. La première porte sur une description détaillée des méthodes d'agrandissement que l'on peut trouver dans la littérature. Nous commençons par présenter les méthodes d'interpolation classiques ayant pour objectif de préserver les fréquences de l'image à agrandir, puis nous détaillons des méthodes récentes de préservation structurelle produisant une meilleure netteté. La deuxième partie constitue la contribution majeure de ce travail en proposant deux nouvelles méthodes d'agrandissement. La première méthode est basée sur la synthèse de similarités détectées sur une représentation pyramidale de l'image. Elle reprend à la base le zoom fractal classique en apportant de nombreuses modifications et améliorations aussi bien dans la phase d'analyse que dans celle de synthèse. Nous vérifions expérimentalement l'hypothèse de préservation des similarités. La deuxième méthode d'agrandissement que nous proposons s'intéresse à l'ensemble admissible des images agrandies d'une image initiale. La condition d'admissibilité repose ici sur la notion de réduction : une image agrandie appartient à l'ensemble des solutions si sa réduction est identique à l'image initiale. Nous étudions différents algorithmes de projection sur cet ensemble. La troisième partie concerne des améliorations et des applications de nos deux méthodes. Tout d'abord, nous améliorons la qualité de l'image agrandie par synthèse de similarités en recherchant celles-ci sur une pyramide en quinconce. Ensuite, nous exploitons la méthode d'agrandissement par induction pour régulariser, vis-à-vis de la contrainte de réduction, les images agrandies par synthèse de similarités. Enfin, nous exploitons également cette méthode pour réaliser un codage hiérarchique de l'image permettant sa transmission progressive sur réseau.

Submitted by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T14:27:18Z No. of bitstreams: 1 ANTUNES_FILHO_Amauri_2016.pdf: 23049873 bytes, checksum: 5246fc4b37bd3364d5abf8cf81fbdb6f (MD5) / Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T14:27:31Z (GMT) No. of bitstreams: 1 ANTUNES_FILHO_Amauri_2016.pdf: 23049873 bytes, checksum: 5246fc4b37bd3364d5abf8cf81fbdb6f (MD5) / Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-06-01T14:39:27Z (GMT) No. of bitstreams: 1 ANTUNES_FILHO_Amauri_2016.pdf: 23049873 bytes, checksum: 5246fc4b37bd3364d5abf8cf81fbdb6f (MD5) / Made available in DSpace on 2017-06-01T14:39:33Z (GMT). No. of bitstreams: 1 ANTUNES_FILHO_Amauri_2016.pdf: 23049873 bytes, checksum: 5246fc4b37bd3364d5abf8cf81fbdb6f (MD5) Previous issue date: 2016-12-09 / Não recebi financiamento / The super-resolution aims to obtain a higher resolution image, using information from one or more low resolution images. There are different applications where super-resolution may be used, such as medical and forensic images. This work proposes a study and development of algorithms, based on Tekalp and Sezan’s algorithm, using the projection onto convex sets theory, in order to obtain super-resolution, therefore obtaining a higher resolution image, from a low resolution images set, with subpixel informations. We proposed the adition of a convex restriction based on Richardon-Lucy’s algorithm, modified to be weighted by Canny’s filter, along with total variation regularization, aiming to restore frequencies lost during high resolution images decimation and degradation processes . Therefore, we have a hybrid approach, that implements spatial and spectral super-resolution simultaneously, based on projection onto convex sets. The obtained results by the proposed algorithms were compared to Tekalp and Sezan’s base algorithm. The visual analysis of the images, along with the mean square error were taken in consideration for comparisons. In development, grayscale images were used, but the methods are extensible for color images. Results showed improvement in the obtained images, with less noise, blurring and more edge definition than the low resolution images. We conclude that the approach has potential for medical applications and forensic computation. / A super-resolução tem por objetivo a obtenção de uma imagem de maior resolução, utilizando informações de uma ou mais imagens de baixa resolução. Existem diferentes aplicações onde a utilização da super-resolução é empregada, como imagens médicas e forenses. A proposta deste trabalho é o estudo e desenvolvimento de algoritmos, baseados no algoritmo de Tekalp e Sezan, que utilizam a teoria de projeções sobre conjuntos convexos com o objetivo de super-resolução, obtendo uma imagem de maior resolução a partir de um conjunto de imagens com informações subpixel. Propomos também, uma restrição convexa baseada no algoritmo de Richardson-Lucy, modificado para ser ponderado pelo filtro de Canny, juntamente com regularização total variation, com o intuito de restaurar frequências perdidas durante os processos de decimação e degradação das imagens de alta resolução. Com isso temos uma abordagem híbrida, que implementa super-resolução espacial e espectral simultaneamente, baseada em projeções sobre conjuntos convexos. Os resultados obtidos pelos algoritmos propostos foram comparados com o algoritmo base de Tekalp e Sezan. Para as comparações, levou-se em consideração a análise visual das imagens juntamente com o erro quadrático médio. No desenvolvimento, foram utilizadas imagens em tons de cinza, mas os métodos são extensíveis para imagens coloridas. Os resultados apresentaram melhoria nas imagens obtidas em relação as imagens de baixa resolução, minimizando o ruído, o borramento e melhor definição das bordas. Concluímos que a abordagem possui potencial para aplicações médicas e em computação forense.