Denoising using projections onto the epigraph set of convex cost functions

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage2713en_US
dc.citation.spage2709en_US
dc.contributor.authorTofighi, Mohammaden_US
dc.contributor.authorKöse, K.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialParis, Franceen_US
dc.date.accessioned2016-02-08T12:26:32Z
dc.date.available2016-02-08T12:26:32Z
dc.date.issued2014en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 27-30 October 2014en_US
dc.descriptionConference Name: International Conference on Image Processing, IEEE 2014en_US
dc.description.abstractA new denoising algorithm based on orthogonal projections onto the epigraph set of a convex cost function is presented. In this algorithm, the dimension of the minimization problem is lifted by one and feasibility sets corresponding to the cost function using the epigraph concept are defined. As the utilized cost function is a convex function in RN, the corresponding epigraph set is also a convex set in RN+1. The denoising algorithm starts with an arbitrary initial estimate in RN+1. At each step of the iterative denoising, an orthogonal projection is performed onto one of the constraint sets associated with the cost function in a sequential manner. The method provides globally optimal solutions for total-variation, ℓ1, ℓ2, and entropic cost functions.1en_US
dc.identifier.doi10.1109/ICIP.2014.7025548en_US
dc.identifier.urihttp://hdl.handle.net/11693/28663
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICIP.2014.7025548en_US
dc.source.titleProceedings of the International Conference on Image Processing, IEEE 2014en_US
dc.subjectDenoisingen_US
dc.subjectEpigraph of a cost functionen_US
dc.subjectProjection onto convex setsen_US
dc.subjectImage denoisingen_US
dc.subjectIterative methodsen_US
dc.subjectOrthogonal functionsen_US
dc.subjectConvex cost functionen_US
dc.subjectOrthogonal projectionen_US
dc.titleDenoising using projections onto the epigraph set of convex cost functionsen_US
dc.typeConference Paperen_US

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