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Image recovery via total variation minimization and related problems

dc.contributor.authorChambolle, Antonin
HAL ID: 184536
ORCID: 0000-0002-9465-4659
dc.contributor.authorLions, Pierre-Louis
dc.date.accessioned2011-05-03T07:40:31Z
dc.date.available2011-05-03T07:40:31Z
dc.date.issued1997
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6146
dc.language.isoenen
dc.subjectTV minimization problemen
dc.subjectimage denoising techniqueen
dc.subject.ddc515en
dc.titleImage recovery via total variation minimization and related problemsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe study here a classical image denoising technique introduced by L. Rudin and S. Osher a few years ago, namely the constrained minimization of the total variation (TV) of the image. First, we give results of existence and uniqueness and prove the link between the constrained minimization problem and the minimization of an associated Lagrangian functional. Then we describe a relaxation method for computing the solution, and give a proof of convergence. After this, we explain why the TV-based model is well suited to the recovery of some images and not of others. We eventually propose an alternative approach whose purpose is to handle the minimization of the minimum of several convex functionals. We propose for instance a variant of the original TV minimization problem that handles correctly some situations where TV fails.en
dc.relation.isversionofjnlnameNumerische Mathematik
dc.relation.isversionofjnlvol76en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate1997
dc.relation.isversionofjnlpages167-188en
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s002110050258en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelAnalyseen


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