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Diplomova praca sa zameriava na oblast zaostrovania obrazkov tvari. V teoretickej casti prace budu prezentovane moderne metody zaostrovania obrazkov pomocou jedineho obrazku a metody editacie obrazkov. Prakticka cast sa zameria na pristupy rekonstrukcie obrazkov zo sekvencie poskodenych obrazkov. Viacere modely neuronovych sieti so vstupom pre viacero obrazkov budu zhotovene a vyhodnotene. Alternativny pristup v podobe balika nastrojov na editaciu obrazkov bude taktiez predstaveny. Tieto nastroje budu vyuzivat najmodernejsie pristupy k editacii obrazkov s cielom spojit vizualne prvky tvari zo vstupnej sekvencie obrazkov do jedneho finalneho vystupu. V zavere prace budu vsetky metody navzajom porovnane.

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Wireless multi-hop relay systems are the newest amendment to the IEEE 802.16 standard for local and metropolitan area networks, else known as WiMAX. Relay systems come in different flavours, based on their capabilities and have the potential to offer many advantages over the single-hop technology. Upcoming broadband wireless technologies, that utilize multi-hop relays, need good network planning and design in order to achieve their full potential. There are two main types of multi-hop relay stations: transparent, which are not able to transmit control information and non-transparent, which have the capability to transmit such information. This study focuses mainly on non-transparent relay stations due to their complexity and ability to operate in a more than two hop environment. Currently, the latest IEEE amendment provides two different frame structures − single and multi-frame − for utilization in multi-hop relay networks, to allocate bandwidth. The purpose of this thesis is to evaluate the two proposed frame structures, in various network scenarios in terms of delay, throughput, rate, and user capacity. In addition, we will discuss some of the issues that need to be considered to cost effectively plan and design a multi-hop relay network. The evaluation methodology that we utilize is in accordance with the Multi-hop Relay System Evaluation Methodology developed by the IEEE 802.16 Broadband Wireless Access Working Group. To evaluate the above frame structures we developed an evaluation model for use in the network simulator 2 (ns2) from University of California Berkeley, by modifying the light WiMAX (LWX) add-on from Taiwan University. Unlike the original LWX module, which supports only transparent relay configurations, our module supports both multi-frame and single frame structures, as well as non-transparent multi-hop relay environments. To our knowledge there is no previous work, which analyzes the performance of the single frame and multi-frame system in multi-hope relay environments using the guidelines from the latest amendment to the standard (IEEE 802.16j-2009). Moreover, there is no publicly available software that will enable the study of such performance. The resulting source code of our work has been made publicly available and can be obtained from our website. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-09-09 23:55:00.995

Multi-frame image super-resolution focuses on reconstructing a high-resolution image from a set of low-resolution images with high similarity. Since super-resolution is an ill-posted problem, regularization techniques are widely used to constrain the minimization function. Combining image prior knowledge with fidelity model, Bayesian-based methods can effectively solve this ill-posed problem, which makes this kind of methods more popular than other methods. Our proposed model is based on maximum a posteriori probability (MAP) estimation. In this thesis, we propose a novel initialization method based on median operator to initialize our estimated high-resolution image. For the fidelity term in our proposed algorithm, the half-quadratic estimation is used to choose error norm adaptively instead of using fixed L1 or L2 norm. Furthermore, for our regularization term, we propose a novel regularization method based on Difference Curvature (DC) and Bilateral Total Variation (BTV) to suppress mixed noises and preserve image edges simultaneously. In our experimental results, synthetic data and real data are both tested to demonstrate the superiority of our proposed method in terms of clearer texture and less noise over other state-of-the-art methods.

<div>This thesis examines the multilinear equations of the calibrated pinhole camera. </div><div>The multilinear equations describe the linear relations between camera parameters and image observations in matrix or tensor formats. </div><div>This thesis includes derivations and analysis of the trilinear equations through the point feature relation. For the four-frame and more than four frame cases, this paper gives derivations and analysis using a combination of the bilinear and trilinear equations to represent general multi-frame point geometry. As a result, a three-frame model (TFM) for general multi-frame point geometry is given. This model provides a concise set of minimal and sufficient equations and minimal unknowns.</div><div> </div><div>Based on the TFM, there are two bundle adjustment (BA) approaches developed. </div><div>The TFM does not involve the object parameters/coordinates necessary and indispensable for the collinearity equation employed by BA. </div><div>The two methods use TFM as the condition equation fully and partially, replacing the collinearity equation. </div><div>One operation using both TFM and the collinearity equation is designed to engage the object structures' prior knowledge. </div><div>The synthetical and real data experiments demonstrate the rationality and validity of the TFM and the two TFM based methods. </div><div>When the unstable estimate of the object structures appears, the TFM-based BA methods have a higher acceptance ratio of the adjustment results. </div>

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Monitoring of public areas and their automatic real-time processing became increasingly significant due to the changing security situation in the world. However, the problem is an analysis of low-quality records, where even the state-of-the-art methods fail in some cases. This work investigates an important area of image similarity – biometric identification based on face image. The work deals primarily with the face super-resolution from a sequence of low-resolution images and it compares this approach to the single-frame methods, that are still considered as the most accurate. A new dataset was created for this purpose, which is directly designed for the multi-frame face super-resolution methods from the low-resolution input sequence, and it is of comparable size with the leading world datasets. The results were evaluated by both a survey of human perception and defined objective metrics. A hypothesis that multi-frame methods achieve better results than single-frame methods was proved by a comparison of both methods. Architectures, source code and the dataset were released. That caused a creation of the basis for future research in this field.