An epigraphical convex optimization approach for multicomponent image restoration using non-local structure tensor

Abstract : TV-like constraints/regularizations are useful tools in variational methods for multicomponent image restoration. In this paper, we design more sophisticated non-local TV constraints which are derived from the structure tensor. The proposed approach allows us to measure the non-local variations, jointly for the different components, through various ℓ_1,p matrix norms with p >= 1. The related convex constrained optimization problems are solved through a novel epigraphical projection method. This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments carried out for color images demonstrate the interest of considering a Non-Local Structure Tensor TV and show that the proposed epigraphical projection method leads to significant improvements in terms of convergence speed over existing numerical solutions.
Type de document :
Communication dans un congrès
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, May 2013, Canada. 2013
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-00826003
Contributeur : Giovanni Chierchia <>
Soumis le : mardi 28 mai 2013 - 16:34:36
Dernière modification le : jeudi 9 février 2017 - 15:20:14
Document(s) archivé(s) le : jeudi 29 août 2013 - 02:45:09

Fichier

cppp_icassp2013.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00826003, version 1

Citation

Giovanni Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet, Béatrice Pesquet-Popescu. An epigraphical convex optimization approach for multicomponent image restoration using non-local structure tensor. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, May 2013, Canada. 2013. <hal-00826003>

Partager

Métriques

Consultations de
la notice

501

Téléchargements du document

169