A memory gradient algorithm for l2-l0 regularization with applications to image restoration - Archive ouverte HAL Access content directly
Conference Papers Year : 2011

A memory gradient algorithm for l2-l0 regularization with applications to image restoration

Abstract

In this paper, we consider a class of differentiable criteria for sparse image recovery problems. The regularization is applied to a linear transform of the target image. As special cases, it includes edge preserving measures or frame analysis potentials. As shown by our asymptotic results, the considered l2-l0 penalties may be employed to approximate solutions to l0 penalized optimization problems. One of the advantages of the approach is that it allows us to derive an efficient Majorize-Minimize Memory Gradient algorithm. The fast convergence properties of the proposed optimization algorithm are illustrated through image restoration examples.
Fichier principal
Vignette du fichier
Chouzenoux_ICIP_11.pdf (537.18 Ko) Télécharger le fichier
Origin Publisher files allowed on an open archive
Loading...

Dates and versions

hal-00687500 , version 1 (14-07-2013)

Identifiers

Cite

Emilie Chouzenoux, Jean-Christophe Pesquet, Hugues Talbot, Anna Jezierska. A memory gradient algorithm for l2-l0 regularization with applications to image restoration. 18th IEEE International Conference on Image Processing (ICIP 2011), Sep 2011, Bruxelles, Belgium. pp.2717-2720, ⟨10.1109/ICIP.2011.6116230⟩. ⟨hal-00687500⟩
178 View
308 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More