Random Sensing of Geometric Images

dc.contributor.author	Peyré, Gabriel HAL ID: 1211
dc.date.accessioned	2009-06-24T13:38:52Z
dc.date.available	2009-06-24T13:38:52Z
dc.date.issued	2006
dc.identifier.uri	https://basepub.dauphine.fr/handle/123456789/446
dc.language.iso	en	en
dc.subject	bandlets
dc.subject	Compressed sensing
dc.subject	best basis
dc.subject.ddc	519	en
dc.title	Random Sensing of Geometric Images
dc.type	Communication / Conférence
dc.description.abstracten	This 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.sponsorshipprivate	oui	en
dc.subject.ddclabel	Probabilités et mathématiques appliquées	en
dc.relation.conftitle	NeuroComp'06
dc.relation.confcity	Pont-à-Mousson
dc.relation.confcountry	FRANCE
dc.description.ssrncandidate	non
dc.description.halcandidate	oui
dc.description.readership	recherche
dc.description.audience	International
dc.date.updated	2017-09-21T13:38:37Z

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