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Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties

hal.structure.identifierDepartment of Electrical and Computer Engineering [Univ California San Diego] [ECE - UC San Diego]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDELEDALLE, Charles-Alban
hal.structure.identifierCentre National de la Recherche Scientifique [CNRS]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierInstitut Montpelliérain Alexander Grothendieck [IMAG]
dc.contributor.authorSALMON, Joseph
hal.structure.identifierInstitut de Mathématiques de Bourgogne [Dijon] [IMB]
hal.structure.identifierCentre National de la Recherche Scientifique [CNRS]
dc.contributor.authorVAITER, Samuel
dc.date.accessioned2024-04-04T02:47:45Z
dc.date.available2024-04-04T02:47:45Z
dc.date.issued2019-06-05
dc.identifier.isbn978-3-030-22367-0
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191677
dc.description.abstractEnIn inverse problems, the use of an l(12) analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. This is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good behavior of the proposed block penalty for refitting.
dc.description.sponsorshipGeneralized Optimal Transport Models for Image processing - ANR-16-CE33-0010
dc.language.isoen
dc.source.titleScale Space and Variational Methods in Computer Vision
dc.subject.enTotal variation
dc.subject.enBias correction
dc.subject.enRefitting
dc.title.enRefitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1007/978-3-030-22368-7_11
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
dc.subject.halMathématiques [math]
dc.description.sponsorshipEuropeNonlocal Methods for Arbitrary Data Sources
bordeaux.page131-143
bordeaux.volume11603
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingScale Space and Variational Methods in Computer Vision
hal.identifierhal-03107463
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03107463v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Scale%20Space%20and%20Variational%20Methods%20in%20Computer%20Vision&rft.date=2019-06-05&rft.volume=11603&rft.spage=131-143&rft.epage=131-143&rft.au=DELEDALLE,%20Charles-Alban&PAPADAKIS,%20Nicolas&SALMON,%20Joseph&VAITER,%20Samuel&rft.isbn=978-3-030-22367-0&rft.genre=unknown


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