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dc.contributor.advisorPınar, Mustafa Çelebi
dc.contributor.authorDizdarer, Tolga
dc.date.accessioned2018-06-13T06:13:39Z
dc.date.available2018-06-13T06:13:39Z
dc.date.copyright2018-06
dc.date.issued2018-06
dc.date.submitted2018-06-06
dc.identifier.urihttp://hdl.handle.net/11693/47580
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 55-59).en_US
dc.description.abstractImage 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.en_US
dc.description.statementofresponsibilityby Tolga Dizdarer.en_US
dc.format.extentxi, 73 leaves : illustrations ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeblurringen_US
dc.subjectImage Restorationen_US
dc.subjectInverse Problemsen_US
dc.subjectNon-Convex Optimizationen_US
dc.titleDeblurring text images affected by multiple kernelsen_US
dc.title.alternativeBirden çok bulanıklaşma unsurundan etkilenmiş metin görüntülerinin netleştrilmesien_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB158474
dc.embargo.release2021-06-05


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