| dc.contributor.author | Peyré, Gabriel | |
| dc.date.accessioned | 2009-06-18T07:52:13Z | |
| dc.date.available | 2009-06-18T07:52:13Z | |
| dc.date.issued | 2009 | |
| dc.identifier.uri | https://basepub.dauphine.fr/handle/123456789/328 | |
| dc.language.iso | en | en |
| dc.subject | signal processing | en |
| dc.subject.ddc | 519 | en |
| dc.title | Manifold Models for Signals and Images | en |
| dc.type | Article accepté pour publication ou publié | |
| dc.description.abstracten | This 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.isversionofjnlname | Computer Vision and Image Understanding | |
| dc.relation.isversionofjnlvol | 113 | en |
| dc.relation.isversionofjnlissue | 2 | en |
| dc.relation.isversionofjnldate | 2009-02 | |
| dc.relation.isversionofjnlpages | 249-260 | en |
| dc.relation.isversionofdoi | http://dx.doi.org/10.1016/j.cviu.2008.09.003 | en |
| dc.identifier.urlsite | http://hal.archives-ouvertes.fr/hal-00359729/en/ | en |
| dc.description.sponsorshipprivate | oui | en |
| dc.relation.isversionofjnlpublisher | Elsevier | en |
| dc.subject.ddclabel | Probabilités et mathématiques appliquées | en |
