Dual constrained TV-based regularization

Camille Couprie 1 Hugues Talbot 1 Jean-Christophe Pesquet 1 Laurent Najman 1 Leo Grady 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 Siemens Corporate Research
Abstract : Algorithms based on the minimization of the Total Variation are prevalent in computer vision. They are used in a variety of applications such as image denoising, compressive sensing and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa-Osher's projection algorithms for TV minimization in a flexible graph data representation by generalizing the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of a fast parallel proximal algorithm, which performs better than the classical TV approach for denoising, and is also applicable to inverse problems such as image deblurring.
Keywords : Proximal algorithm inverse problems image denoising convex optimization image restoration
Type de document :
Communication dans un congrès
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, May 2011, Prague, Czech Republic. IEEE, pp.945 - 948, 2011, <10.1109/ICASSP.2011.5946561>
Liste complète des métadonnées

https://hal-upec-upem.archives-ouvertes.fr/hal-00744071
Contributeur : Laurent Najman <>
Soumis le : lundi 22 octobre 2012 - 11:44:19
Dernière modification le : lundi 22 octobre 2012 - 14:42:43
Document(s) archivé(s) le : samedi 17 décembre 2016 - 03:02:16

Fichier

DCTV-ICASSP.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

LIGM_A3SI | LIGM | ENPC-LIGM | PARISTECH | ENPC | LIGM_SIGNAL | UNIV-MLV | UPEC-UPEM

Citation

Camille Couprie, Hugues Talbot, Jean-Christophe Pesquet, Laurent Najman, Leo Grady. Dual constrained TV-based regularization. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, May 2011, Prague, Czech Republic. IEEE, pp.945 - 948, 2011, <10.1109/ICASSP.2011.5946561>. <hal-00744071>

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de
la notice

379

Téléchargements du document

92

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD