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dc.contributor.authorKöse, Kıvançen_US
dc.contributor.authorCevher V.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialKyoto, Japanen_US
dc.date.accessioned2016-02-08T12:12:39Z
dc.date.available2016-02-08T12:12:39Z
dc.date.issued2012en_US
dc.identifier.issn1520-6149
dc.identifier.urihttp://hdl.handle.net/11693/28155
dc.descriptionDate of Conference: 25-30 March 2012en_US
dc.description.abstractWe propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that are widely used in signal processing. We mathematically define the FV problem, and solve it using alternating projections in space and transform domains. We provide a globally convergent algorithm based on the projections onto convex sets approach. We apply to our algorithm to real denoising problems and compare it with the total variation recovery. © 2012 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICASSP.2012.6288628en_US
dc.subjectFiltered variationen_US
dc.subjectAlternating projectionsen_US
dc.subjectDe-noisingen_US
dc.subjectDenoising problemsen_US
dc.subjectDiscrete-time filtersen_US
dc.subjectFiltered variationen_US
dc.subjectGlobally convergenten_US
dc.subjectIll poseden_US
dc.subjectProjection onto convex setsen_US
dc.subjectProjections onto convex setsen_US
dc.subjectSparse signalsen_US
dc.subjectTotal variationen_US
dc.subjectTransform domainen_US
dc.subjectVariation methoden_US
dc.subjectAlgorithmsen_US
dc.subjectSet theoryen_US
dc.subjectSignal processingen_US
dc.subjectProblem solvingen_US
dc.titleFiltered Variation method for denoising and sparse signal processingen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage3329en_US
dc.citation.epage3332en_US
dc.identifier.doi10.1109/ICASSP.2012.6288628en_US
dc.publisherIEEEen_US


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