DELEDALLE, Charles-Alban; PAPADAKIS, Nicolas; SALMON, Joseph; VAITER, Samuel

doi:10.1007/978-3-030-22368-7_11

Métadonnées

Licence d’utilisation du document

DELEDALLE, Charles-Alban
Department of Electrical and Computer Engineering [Univ California San Diego] [ECE - UC San Diego]
Institut de Mathématiques de Bordeaux [IMB]

PAPADAKIS, Nicolas
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bordeaux [IMB]

SALMON, Joseph
Institut Montpelliérain Alexander Grothendieck [IMAG]

Langue

Chapitre d'ouvrage

Ce document a été publié dans

Scale Space and Variational Methods in Computer Vision, Scale Space and Variational Methods in Computer Vision. 2019-06-05, vol. 11603, p. 131-143

Résumé en anglais

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.< Réduire

Mots clés en anglais

Total variation

Bias correction

Refitting

URI

https://oskar-bordeaux.fr/handle/20.500.12278/191677

DOI

http://dx.doi.org/10.1007/978-3-030-22368-7_11

Projet Européen

Nonlocal Methods for Arbitrary Data Sources

Project ANR

Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010

Origine

Importé de hal

Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251