Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties
| hal.structure.identifier | Department of Electrical and Computer Engineering [Univ California San Diego] [ECE - UC San Diego] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DELEDALLE, Charles-Alban | |
| hal.structure.identifier | Centre National de la Recherche Scientifique [CNRS] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | PAPADAKIS, Nicolas | |
| hal.structure.identifier | Institut Montpelliérain Alexander Grothendieck [IMAG] | |
| dc.contributor.author | SALMON, Joseph | |
| hal.structure.identifier | Institut de Mathématiques de Bourgogne [Dijon] [IMB] | |
| hal.structure.identifier | Centre National de la Recherche Scientifique [CNRS] | |
| dc.contributor.author | VAITER, Samuel | |
| dc.date.accessioned | 2024-04-04T02:47:45Z | |
| dc.date.available | 2024-04-04T02:47:45Z | |
| dc.date.issued | 2019-06-05 | |
| dc.identifier.isbn | 978-3-030-22367-0 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191677 | |
| dc.description.abstractEn | In 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.sponsorship | Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010 | |
| dc.language.iso | en | |
| dc.source.title | Scale Space and Variational Methods in Computer Vision | |
| dc.subject.en | Total variation | |
| dc.subject.en | Bias correction | |
| dc.subject.en | Refitting | |
| dc.title.en | Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties | |
| dc.type | Chapitre d'ouvrage | |
| dc.identifier.doi | 10.1007/978-3-030-22368-7_11 | |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
| dc.subject.hal | Mathématiques [math] | |
| dc.description.sponsorshipEurope | Nonlocal Methods for Arbitrary Data Sources | |
| bordeaux.page | 131-143 | |
| bordeaux.volume | 11603 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.title.proceeding | Scale Space and Variational Methods in Computer Vision | |
| hal.identifier | hal-03107463 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03107463v1 | |
| bordeaux.COinS | ctx_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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