Thesis (Ph. D.), Oregon Graduate Institute of Science & Technology, 1995.

Neste trabalho é proposta uma adaptação do método de Mínimos Quadrados aplicado à reconstrução de imagens com Super-Resolução, visando a reconstrução de vídeo (sequências de imagens) em tempo real. Resultados demonstram que a implementação proposta pode apresentar um desempenho melhor que o algoritmo de menor custo computacional da literatura, com um pequeno incremento no número de operações. Por fim, a estrutura proposta sugere viabilidade de análise. De posse de um modelo teórico de comportamento, parâmetros ótimos de projeto podem ser obtidos melhorando ainda mais o desempenho do algoritmo. / Submitted by Ana Guimarães Pereira (agpereir@ucs.br) on 2015-02-11T16:26:47Z No. of bitstreams: 1 Dissertacao Rodrigo Brandalise.pdf: 882370 bytes, checksum: f5b09a60e986391a1d064bc07ccb57e6 (MD5) / Made available in DSpace on 2015-02-11T16:26:47Z (GMT). No. of bitstreams: 1 Dissertacao Rodrigo Brandalise.pdf: 882370 bytes, checksum: f5b09a60e986391a1d064bc07ccb57e6 (MD5) / In this work an adaptation of the Least Squares method applied to Super-Resolution image reconstruction is proposed aiming real time video (image sequences) reconstruction. Results demonstrate that the proposed implementation presents a better performance than the algorithm with the lower computational cost presented in the literature, considering a small increase in the number of operations. Finally, the proposed structure suggests feasibility of analysis. A theoretical model for the algorithm behavior can leads to an optimal parameters design yielding further improvements in the algorithm performance.

Neste trabalho é proposta uma adaptação do método de Mínimos Quadrados aplicado à reconstrução de imagens com Super-Resolução, visando a reconstrução de vídeo (sequências de imagens) em tempo real. Resultados demonstram que a implementação proposta pode apresentar um desempenho melhor que o algoritmo de menor custo computacional da literatura, com um pequeno incremento no número de operações. Por fim, a estrutura proposta sugere viabilidade de análise. De posse de um modelo teórico de comportamento, parâmetros ótimos de projeto podem ser obtidos melhorando ainda mais o desempenho do algoritmo. / In this work an adaptation of the Least Squares method applied to Super-Resolution image reconstruction is proposed aiming real time video (image sequences) reconstruction. Results demonstrate that the proposed implementation presents a better performance than the algorithm with the lower computational cost presented in the literature, considering a small increase in the number of operations. Finally, the proposed structure suggests feasibility of analysis. A theoretical model for the algorithm behavior can leads to an optimal parameters design yielding further improvements in the algorithm performance.

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For many digital image/video processing applications increasing the spatial resolution is highly beneficial. At higher resolution, TV pictures look more natural and pleasing to the eye, computer vision tasks such as object detection and tracking can be performed with higher precision, medical diagnoses can be made with a higher confidence, security cameras can offer better identification, and satellite imagery can be interpreted with higher accuracy. As such, spatial resolution is an influential parameter in many mainstream imaging applications, and resolution enhancement task naturally arises as a means of increasing the effectiveness of any imaging system used in the mentioned applications. In this thesis, we concentrate on two enhancement problems of practical importance, namely, low-complexity resolution enhancement for customer grade flat panel televisions, and resolution enhancement of noisy high-dimensional hyperspectral imagery. For TV resolution enhancement our main concern is keeping computational complexity at a minimum. The hardware limitations of average customer grade televisions effectively rule out a multi-frame approach. Hence, we take a low-complexity single-frame approach based on exploiting natural image characteristics. For hyperspectral imagery we take advantage of multiple observations in a modified superresolution framework. Here the main challenges are the high dimensionality of hyperspectral data and the noise present in all spectral bands. We design a physical model of the hyperspectral image acquisition process, and based on this model we formulate an iterative resolution enhancement algorithm.

In this thesis, methods for achieving finer scale multi-spectral classification through the use of super resolution (SR) techniques are investigated. A new super resolution algorithm Maximum a Posteriori based on the universal hidden Markov tree model (MAP-uHMT) is developed which can be applied successfully to super-resolve each multi-spectral channel before classification by standard methods. It is believed that this is the first time that a true super resolution algorithm has been applied to multi-spectral classification, and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This improved elastic image registration method can deal with a global affine warping and local shift translations based on coarse to fine pyramid levels. The experimental results show that it can provide good registration accuracy in less computational time than comparable methods. Maximum a posteriori (MAP) is adopted to deal with the ill-conditioned problem of super resolution, wherein a prior is constructed based on the universal hidden Markov tree (uHMT) model in the wavelet domain. In order to test this prior for MAP estimation, it is first tested in the simpler and typically ill-conditioned problem of image denoising. Experimental results illustrate that this new image denoising method achieves good performance for the test images. The new prior is then extended to SR. By combining with the new elastic image registration algorithm, MAP-uHMT can super resolve both some natural video frames and remote sensing images. Test results with both synthetic data and real data show that this method achieves super resolution both visually and quantitatively. In order to show that MAPuHMT is also applicable more widely, it is tested on a sequence of long-range surveillance images captured under conditions of atmospheric turbulence distortion. The results suggest that super resolution may have been achieved in this application also.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 70-73).

The objective of the proposed research is to develop high performance, low computational complexity resolution enhancement and demosaicing algorithms. Our approach to both problems is to find creative ways to incorporate edge information into the algorithm design. However, in contrast with the usual edge directed approaches, we do not try to detect edge presence and orientation explicitly. For the image interpolation problem, we study the relationship between low resolution and high resolution pixels, and derive a general interpolation formula to be used on all pixels. This simple interpolation algorithm is able to generate sharp edges in any orientation. We also propose a simple 3 by 3 filter that quantifies local luminance transition and apply it to the demosaicing problem. Additionally, we propose a gradient based directional demosaicing method that does not require setting any thresholds. We show that the performance of this algorithm can be improved by using multiscale gradients. Finally, we address the low spectral correlation demosaicing problem by proposing a new family of hybrid color filter array (CFA) patterns and a local algorithm that is two orders of magnitude faster than a comparable non-local solution while offering the same level of performance.