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

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.

Mots clés: Proximal algorithm; inverse problems; image denoising; convex optimization; image restoration

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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. pp.945 - 948, ⟨10.1109/ICASSP.2011.5946561⟩. ⟨hal-00744071⟩