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dc.contributor.authorStarck, Jean-Luc
dc.contributor.authorFadili, Jalal
dc.contributor.authorPeyré, Gabriel
dc.date.accessioned2010-07-22T15:09:42Z
dc.date.available2010-07-22T15:09:42Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4651
dc.language.isoenen
dc.subjectAdaptive morphological component analysisen
dc.subjectsparsityen
dc.subjectimage separationen
dc.subjectinpaintingen
dc.subjectdictionary learningen
dc.subjectcartoon imagesen
dc.subjecttextureen
dc.subjectwaveletsen
dc.subject.ddc621.3en
dc.titleLearning the Morphological Diversityen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis article proposes a new method for image separation into a linear combination of morphological components. Sparsity in global dictionaries is used to extract the cartoon and oscillating content of the image. Complicated texture patterns are extracted by learning adapted local dictionaries that sparsify patches in the image. These global and local sparsity priors together with the data fidelity define a non-convex energy and the separation is obtained as a stationary point of this energy. This variational optimization is extended to solve more general inverse problems such as inpainting. A new adaptive morphological component analysis algorithm is derived to find a stationary point of the energy. 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.relation.isversionofjnlnameSIAM Journal on Imaging Sciences
dc.relation.isversionofjnlvol3
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2010
dc.relation.isversionofjnlpages646-669
dc.relation.isversionofdoihttp://dx.doi.org/10.1137/090770783
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00415782/fr/en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSIAMen
dc.subject.ddclabelTraitement du signalen


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