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Learning Adapted Dictionaries for Geometry and Texture Separation

dc.contributor.authorStarck, Jean-Luc
dc.contributor.authorFadili, Jalal
HAL ID: 15510
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.date.accessioned2009-07-08T12:34:05Z
dc.date.available2009-07-08T12:34:05Z
dc.date.issued2007-09
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/974
dc.language.isoenen
dc.subjecttotal variationen
dc.subjectwaveletsen
dc.subjectlearning dictionaryen
dc.subjectsparsityen
dc.subjectImage separationen
dc.subject.ddc519en
dc.titleLearning Adapted Dictionaries for Geometry and Texture Separationen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherCNRS - Université de Caen - Ecole Nationale Supérieure d'Ingénieurs de Caen;France
dc.description.abstractenThis article proposes a new method for image separation into a linear combination of morphological components. This method is applied to decompose an image into meaningful cartoon and textural layers and is used to solve more general inverse problems such as image inpainting. For each of these components, a dictionary is learned from a set of exemplar images. Each layer is characterized by a sparse expansion in the corresponding dictionary. The separation inverse problem is formalized within a variational framework as the optimization of an energy functional. The morphological component analysis algorithm allows to solve iteratively this optimization problem under sparsity-promoting penalties. Using adapted dictionaries learned from data allows to circumvent some difficulties faced by fixed dictionaries. Numerical results demonstrate that this adaptivity is indeed crucial to capture complex texture patterns.en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00365601/en/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.conftitleSPIE Wavelets XIIen
dc.relation.confdate2007-08
dc.relation.confcitySan Diego, CAen
dc.relation.confcountryEtats-Unis


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