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Best Basis Compressed Sensing

dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.date.accessioned2013-11-22T12:39:47Z
dc.date.available2013-11-22T12:39:47Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/12144
dc.language.isoenen
dc.subjectbandletsen
dc.subjectbest basisen
dc.subjectcompressed sensingen
dc.subjectcosine packetsen
dc.subjectsparsityen
dc.subjectwavelet packetsen
dc.subject.ddc003en
dc.titleBest Basis Compressed Sensingen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper proposes a best basis extension of compressed sensing recovery. Instead of regularizing the compressed sensing inverse problem with a sparsity prior in a fixed basis, our framework makes use of sparsity in a tree-structured dictionary of orthogonal bases. A new iterative thresholding algorithm performs both the recovery of the signal and the estimation of the best basis. The resulting reconstruction from compressive measurements optimizes the basis to the structure of the sensed signal. Adaptivity is crucial to capture the regularity of complex natural signals. Numerical experiments on sounds and geometrical images indeed show that this best basis search improves the recovery with respect to fixed sparsity priors.en
dc.relation.isversionofjnlnameIEEE Transactions on Signal Processing
dc.relation.isversionofjnlvol58en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2010
dc.relation.isversionofjnlpages2613-2622en
dc.relation.isversionofdoihttp://dx.doi.org/10.1109/TSP.2010.2042490en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00365607/en/en
dc.relation.isversionofjnlpublisherIEEEen
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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