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dc.contributor.authorPeyré, Gabriel
dc.date.accessioned2009-06-18T07:52:13Z
dc.date.available2009-06-18T07:52:13Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/328
dc.language.isoenen
dc.subjectsignal processingen
dc.subject.ddc519en
dc.titleManifold Models for Signals and Imagesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis article proposes a new class of models for natural signals and images. These models constrain the set of patches extracted from the data to analyze to be close to a low dimensional manifold. This manifold structure is detailed for various ensembles suitable for natural signals, images and textures modeling. These manifolds provide a low-dimensional parameterization of the local geometry of these datasets. These manifold models can be used to regularize inverse problems in signal and image processing. The restored signal is represented as a smooth curve or surface traced on the manifold that matches the forward measurements. A manifold pursuit algorithm computes iteratively a solution of the manifold regularization problem. Numerical simulations on inpainting and compressive sensing inversion show that manifolds models bring an improvement for the recovery of data with geometrical features.en
dc.relation.isversionofjnlnameComputer Vision and Image Understanding
dc.relation.isversionofjnlvol113en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2009-02
dc.relation.isversionofjnlpages249-260en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.cviu.2008.09.003en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00359729/en/en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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