Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2019

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

Résumé

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.
Fichier non déposé

Dates et versions

hal-03107463 , version 1 (12-01-2021)

Identifiants

Citer

Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. Refitting Solutions Promoted by $$\ell _{12}$$ Sparse Analysis Regularizations with Block Penalties. Scale Space and Variational Methods in Computer Vision, 11603, , pp.131-143, 2019, 978-3-030-22367-0. ⟨10.1007/978-3-030-22368-7_11⟩. ⟨hal-03107463⟩
43 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More