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dc.contributor.authorPeyré, Gabriel
dc.contributor.authorFadili, Jalal
dc.date.accessioned2010-12-06T11:45:07Z
dc.date.available2010-12-06T11:45:07Z
dc.date.issued2011
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5241
dc.language.isoenen
dc.subjectDictionary learningen
dc.subjectdenoisingen
dc.subjectanalysis prioren
dc.subjecttotal variationen
dc.subject.ddc621.3en
dc.titleLearning Analysis Sparsity Priorsen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherGREYC, CNRS-ENSICAEN-Universit´e de Caen;France
dc.description.abstractenThis paper introduces a novel approach to learn a dictionary in a sparsity-promoting analysis-type prior. The dictionary is opti- mized in order to optimally restore a set of exemplars from their degraded noisy versions. Towards this goal, we cast our prob- lem as a bilevel programming problem for which we propose a gradient descent algorithm to reach a stationary point that might be a local minimizer. When the dictionary analysis operator specializes to a convolution, our method turns out to be a way of learning generalized total variation-type prior. Applications to 1-D signal denoising are reported and potential applicability and extensions are discussed.en
dc.identifier.citationpages4en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00542016/fr/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelTraitement du signalen
dc.relation.conftitle9th International Conference on Sampling Theory and Applications (SAMPTA 2011)
dc.relation.confdate2011-05
dc.relation.confcitySingapour
dc.relation.confcountrySingapour


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