Dual constrained TV-based regularization

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 GilboaOsher’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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Camille Couprie, Hugues Talbot, Jean-Christophe Pesquet, Laurent Najman, Leo J. Grady
• Keywords: Classical Tv Approach, ICASSP 2011, Parallel Proximal Algorithm, Signal Processing, TV Dual Framework