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dc.contributor.authorPeyré, Gabriel
dc.date.accessioned2009-06-24T13:38:52Z
dc.date.available2009-06-24T13:38:52Z
dc.date.issued2006
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/446
dc.language.isoenen
dc.subjectbandlets
dc.subjectCompressed sensing
dc.subjectbest basis
dc.subject.ddc519en
dc.titleRandom Sensing of Geometric Images
dc.typeCommunication / Conférence
dc.description.abstractenThis paper proposes an extension of compressed sensing that allows to express the sparsity prior in a dictionary of bases. This enables the use of the universal sampling strategy of compressed sensing together with an adaptive recovery process that adapts the basis to the structure of the sensed signal. A fast greedy scheme is used during reconstruction to estimate the best basis using an iterative refinement. Numerical experiments on geometrical images show that adaptivity is indeed crucial to capture the structures of complex natural signals.
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.conftitleNeuroComp'06
dc.relation.confcityPont-à-Mousson
dc.relation.confcountryFRANCE
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-09-21T13:38:37Z


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