Dizdarer, Tolga2018-06-132018-06-132018-062018-062018-06-06http://hdl.handle.net/11693/47580Cataloged from PDF version of article.Includes bibliographical references (leaves 55-59).Image deblurring is one of the widely studied and challenging problems in image recovery. It is an estimation problem dealing with restoration of a linearly transformed image that is additional disturbed with noise. In our research, we propose a new method to solve deblurring problems on text images a ected by multiple kernels. In our approach we focus speci cally on almost binary images that have speci c intensity structures. First, we propose a non-convex non-blind deblurring model and provide an e cient algorithm that can restore a text-like image when the blurring kernel is known. Then we provide our alternate setting, the semi-blind problem, where a kernel is determined as a linear combination of multiple kernels. We propose how one can attack the deblurring problem by using dictionaries that are constructed using any prior information about the kernel. We propose a semi-blind deblurring model that can estimate optimal kernel using the elements of the dictionary. We consider a unique algorithm structure that favors regularizing the iterations through scaled parameter values and argue the advantages of this approach. Lastly, we consider some speci c problems that are commonly used in the literature where one can utilize our alternate problem setting. We argue how one can construct a dictionary that can maximize the utility gained by the prior information regarding the blurring process and present the performance of our model in such cases.xi, 73 leaves : illustrations ; 30 cm.Englishinfo:eu-repo/semantics/openAccessDeblurringImage RestorationInverse ProblemsNon-Convex OptimizationThesisB158474