Denoising images corrupted by impulsive noise using projections onto the epigraph set of the total variation function (PES-TV)

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage48en_US
dc.citation.spage41en_US
dc.citation.volumeNumber9en_US
dc.contributor.authorTofighi M.en_US
dc.contributor.authorKose, K.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.date.accessioned2016-02-08T10:57:54Z
dc.date.available2016-02-08T10:57:54Z
dc.date.issued2015en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn this article, a novel algorithm for denoising images corrupted by impulsive noise is presented. Impulsive noise generates pixels whose gray level values are not consistent with the neighboring pixels. The proposed denoising algorithm is a two-step procedure. In the first step, image denoising is formulated as a convex optimization problem, whose constraints are defined as limitations on local variations between neighboring pixels. We use Projections onto the Epigraph Set of the TV function (PES-TV) to solve this problem. Unlike other approaches in the literature, the PES-TV method does not require any prior information about the noise variance. It is only capable of utilizing local relations among pixels and does not fully take advantage of correlations between spatially distant areas of an image with similar appearance. In the second step, a Wiener filtering approach is cascaded to the PES-TV-based method to take advantage of global correlations in an image. In this step, the image is first divided into blocks and those with similar content are jointly denoised using a 3D Wiener filter. The denoising performance of the proposed two-step method was compared against three state-of-the-art denoising methods under various impulsive noise models.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:57:54Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2015en
dc.identifier.doi10.1007/s11760-015-0827-8en_US
dc.identifier.issn1863-1703
dc.identifier.urihttp://hdl.handle.net/11693/26297
dc.language.isoEnglishen_US
dc.publisherSpringer U Ken_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s11760-015-0827-8en_US
dc.source.titleSignal, Image and Video Processingen_US
dc.subjectDenoisingen_US
dc.subjectEpigraph setsen_US
dc.subjectAlgorithmsen_US
dc.subjectConvex optimizationen_US
dc.subjectImpulse noiseen_US
dc.subjectInverse problemsen_US
dc.subjectOptimizationen_US
dc.subjectPixelsen_US
dc.subjectProblem solvingen_US
dc.subjectConvex optimization problemsen_US
dc.subjectDe-noisingen_US
dc.subjectDe-noising algorithmen_US
dc.subjectGlobal correlationen_US
dc.subjectGray level valuesen_US
dc.subjectImpulsive noise modelsen_US
dc.subjectTwo-step procedureen_US
dc.subjectImage denoisingen_US
dc.titleDenoising images corrupted by impulsive noise using projections onto the epigraph set of the total variation function (PES-TV)en_US
dc.typeArticleen_US

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