Deblurring with Framelets in the Sparse Analysis Setting

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dc.contributor.authorDanniels, Travis
dc.contributor.supervisorGulliver, T. Aaron
dc.date.accessioned2013-12-23T16:12:14Z
dc.date.available2013-12-23T16:12:14Z
dc.date.copyright2013en_US
dc.date.issued2013-12-23
dc.degree.departmentDepartment of Electrical and Computer Engineeringen_US
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractIn this thesis, algorithms for blind and non-blind motion deblurring of digital images are proposed. The non-blind algorithm is based on a convex program consisting of a data fitting term and a sparsity-promoting regularization term. The data fitting term is the squared l_2 norm of the residual between the blurred image and the latent image convolved with a known blur kernel. The regularization term is the l_1 norm of the latent image under a wavelet frame (framelet) decomposition. This convex program is solved with the first-order primal-dual algorithm proposed by Chambolle and Pock. The proposed blind deblurring algorithm is based on the work of Cai, Ji, Liu, and Shen. It works by embedding the proposed non-blind algorithm in an alternating minimization scheme and imposing additional constraints in order to deal with the challenging non-convex nature of the blind deblurring problem. Numerical experiments are performed on artificially and naturally blurred images, and both proposed algorithms are found to be competitive with recent deblurring methods.en_US
dc.description.proquestcode0544en_US
dc.description.proquestemailtdanniels@gmail.comen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/5107
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectimage processingen_US
dc.subjectinverse problemsen_US
dc.subjectdeblurringen_US
dc.subjectconvex optimizationen_US
dc.subjectsparsityen_US
dc.subjectwaveletsen_US
dc.titleDeblurring with Framelets in the Sparse Analysis Settingen_US
dc.typeThesisen_US

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