Dual constrained TV-based regularization

Camille Couprie; Hugues Talbot; Jean-Christophe Pesquet; Laurent Najman; Leo Grady

doi:10.1109/ICASSP.2011.5946561

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

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Signal and Image Processing

Engineering Sciences [physics] / Signal and Image processing

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https://hal-upec-upem.archives-ouvertes.fr/hal-00744071
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