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We propose a method of adaptive image denoising using a support vector machine (SVM) classifier to select between multiple well-performing contemporary denoising algorithms for each pixel of a noisy image. We begin by proposing a simple method for realistically generating noisy images, and also describe a number of novel and pre-existing features based on seam energy, local colour, and saliency which are used as classifier inputs. Our SVM strategic image denoising (SVMSID) results demonstrate better image quality than either candidate denoising algorithm for images of moderate noise level, as measured using the perceptually-based quaternion structural similarity image metric (QSSIM). We also demonstrate a modified training point selection method to improve robustness across many noise levels, and propose various extensions to SVMSID for further exploration. / Thesis / Master of Applied Science (MASc)

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Nous étudions dans cette thèse une représentation particulière de signaux fondée sur une méthode d’apprentissage statistique, qui consiste à modéliser des données comme combinaisons linéaires de quelques éléments d’un dictionnaire appris. Ceci peut être vu comme une extension du cadre classique des ondelettes, dont le but est de construire de tels dictionnaires (souvent des bases orthonormales) qui sont adaptés aux signaux naturels. Un succès important de cette approche a été sa capacité à modéliser des imagettes, et la performance des méthodes de débruitage d’images fondées sur elle. Nous traitons plusieurs questions ouvertes, qui sont reliées à ce cadre : Comment apprendre efficacement un dictionnaire ? Comment enrichir ce modèle en ajoutant une structure sous-jacente au dictionnaire ? Est-il possible d’améliorer les méthodes actuelles de traitement d’image fondées sur cette approche ? Comment doit-on apprendre le dictionnaire lorsque celui-ci est utilisé pour une tâche autre que la reconstruction de signaux ? Y a-t-il des applications intéressantes de cette méthode en vision par ordinateur ? Nous répondons à ces questions, avec un point de vue multidisciplinaire, en empruntant des outils d’apprentissage statistique, d’optimisation convexe et stochastique, de traitement des signaux et des images, de vison par ordinateur, mais aussi d'optimisation sur des graphes. / We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework: How to efficiently optimize the dictionary? How can the model be enriched by adding a structure to the dictionary? Can current image processing tools based on this method be further improved? How should one learn the dictionary when it is used for a different task than signal reconstruction? How can it be used for solving computer vision problems? We answer these questions with a multidisciplinarity approach, using tools from statistical machine learning, convex and stochastic optimization, image and signal processing, computer vision, but also optimization on graphs.

Since the birth of charge coupled devices (CCD) and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors, pixel pitch of digital image sensors has been continuously shrinking to meet the resolution and size requirements of the cameras. However, shrinking pixels reduces the maximum number of photons a sensor can hold, a phenomenon broadly known as the full-well capacity limit. The drop in full-well capacity causes drop in signal-to-noise ratio and dynamic range.<div><br></div><div>The Quanta Image Sensor (QIS) is a class of solid-state image sensors proposed by Eric Fossum in 2005 as a potential solution for the limited full-well capacity problem. QIS is envisioned to be the next generation image sensor after CCD and CMOS since it enables sub-diffraction-limit pixels without the inherited problems of pixel shrinking. Equipped with a massive number of detectors that have single-photon sensitivity, the sensor counts the incoming photons and triggers a binary response “1” if the photon count exceeds a threshold, or “0” otherwise. To acquire an image, the sensor oversamples the space and time to generate a sequence of binary bit maps. Because of this binary sensing mechanism, the full-well capacity, signal-to-noise ratio and the dynamic range can all be improved using an appropriate image reconstruction algorithm. The contribution of this thesis is to address three image processing problems in QIS: 1) Image reconstruction, 2) Threshold design and 3) Color filter array design.</div><div><br></div><div>Part 1 of the thesis focuses on reconstructing the latent grayscale image from the QIS binary measurements. Image reconstruction is a necessary step for QIS because the raw binary measurements are not images. Previous methods in the literature use iterative algorithms which are computationally expensive. By modeling the QIS binary measurements as quantized Poisson random variables, a new non-iterative image reconstruction method based on the Transform-Denoise framework is proposed. Experimental results show that the new method produces better quality images while requiring less computing time.</div><div><br></div><div>Part 2 of the thesis considers the threshold design problem of a QIS. A spatially-varying threshold can significantly improve the reconstruction quality and the dynamic range. However, no known method of how to achieve this can be found in the literature. The theoretical analysis of this part shows that the optimal threshold should match with the underlying pixel intensity. In addition, the analysis proves the existence of a set of thresholds around the optimal threshold that give asymptotically unbiased reconstructions. The asymptotic unbiasedness has a phase transition behavior. A new threshold update scheme based on this idea is proposed. Experimentally, the new method can provide good estimates of the thresholds with less computing budget compared to existing methods.</div><div><br></div><div>Part 3 of the thesis extends QIS capabilities to color imaging by studying how a color filter array should be designed. Because of the small pixel pitch of QIS, crosstalk between neighboring pixels is inevitable and should be considered when designing the color filter arrays. However, optimizing the light efficiency while suppressing aliasing and crosstalk in a color filter array are conflicting tasks. A new optimization framework is proposed to solve the problem. The new framework unifies several mainstream design criteria while offering generality and flexibility. Extensive experimental comparisons demonstrate the effectiveness of the framework.</div>

Imagens digitais são utilizadas para diversas finalidades, variando de uma simples foto com os amigos até a identificação de doenças em exames médicos. Por mais que as tecnologias de captura de imagens tenham evoluído, toda imagem adquirida digitalmente possui um ruído intrínseco a ela que normalmente é adquirido durante os processo de captura ou transmissão da imagem. O grande desafio neste tipo de problema consiste em recuperar a imagem perdendo o mínimo possível de características importantes da imagem, como cantos, bordas e texturas. Este trabalho propõe uma abordagem baseada em um Algoritmo Genético Híbrido (AGH) para lidar com este tipo de problema. O AGH combina um algoritmo genético com alguns dos melhores métodos de supressão de ruídos em imagens encontrados na literatura, utilizando-os como operadores de busca local. O AGH foi testado em imagens normalmente utilizadas como benchmark corrompidas com um ruído branco aditivo Gaussiano (N; 0), com diversos níveis de desvio padrão para o ruído. Seus resultados, medidos pelas métricas PSNR e SSIM, são comparados com os resultados obtidos por diferentes métodos. O AGH também foi testado para recuperar imagens SAR (Synthetic Aperture Radar), corrompidas com um ruído Speckle multiplicativo, e também teve seus resultados comparados com métodos especializados em recuperar imagens SAR. Através dessa abordagem híbrida, o AGH foi capaz de obter resultados competitivos em ambos os tipos de testes, chegando inclusive a obter melhores resultados em diversos casos em relação aos métodos da literatura. / Digital images are used for many purposes, ranging from a simple picture with friends to the identification of diseases in medical exams. Even though the technology for acquiring pictures has been evolving, every image digitally acquired has a noise intrinsic to it that is normally gotten during the processes of transmission or capture of the image. A big challenge in this kind of problem consists in recovering the image while losing the minimum amount of important features of the image, such as corners, borders and textures. This work proposes an approach based on a Hybrid Genetic Algorithm (HGA) to deal with this kind of problem. The HGA combines a genetic algorithm with some of the best image denoising methods found in literature, using them as local search operators. The HGA was tested on benchmark images corrupted with an additive white Gaussian noise (N;0) with many levels of standard deviation for the noise. The HGAs results, which were measured by the PSNR and SSIM metrics, were compared to the results obtained by different methods. The HGA was also tested to recover SAR (Synthetic Aperture Radar) images that were corrupted by a multiplicative Speckle noise and had its results compared against the results by other methods specialized in recovering with SAR images. Through this hybrid approach, the HGA was able to obtain results competitive in both types of tests, even being able to obtain the best results in many cases, when compared to the other methods found in the literature.

Accurate and fast machine learning (ML) algorithms are highly vital in artificial intelligence (AI) applications. In complex dataset problems, traditional ML methods such as radial basis function neural network (RBFN), sparse coding (SC) using dictionary learning, and particle swarm optimization (PSO) provide trivial results, large structure, slow training, and/or slow testing. This dissertation introduces four novel ML techniques: the multi-column RBFN network (MCRN), the projected dictionary learning algorithm (PDL) and the multi-column adaptive and non-adaptive particle swarm optimization techniques (MC-APSO and MC-PSO). These novel techniques provide efficient alternatives for traditional ML techniques. Compared to traditional ML techniques, the novel ML techniques demonstrate more accurate results, faster training and testing timing, and parallelized structured solutions. MCRN deploys small RBFNs in a parallel structure to speed up both training and testing. Each RBFN is trained with a subset of the dataset and the overall structure provides results that are more accurate. PDL introduces a conceptual dictionary learning method in updating the dictionary atoms with the reconstructed input blocks. This method improves the sparsity of extracted features and hence, the image denoising results. MC-PSO and MC-APSO provide fast and more accurate alternatives to the PSO and APSO slow evolutionary techniques. MC-PSO and MC-APSO use multi-column parallelized RBFN structure to improve results and speed with a wide range of classification dataset problems. The novel techniques are trained and tested using benchmark dataset problems and the results are compared with the state-of-the-art counterpart techniques to evaluate their performance. Novel techniques’ results show superiority over techniques in accuracy and speed in most of the experimental results, which make them good alternatives in solving difficult ML problems.