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〉
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Dernière modification le : jeudi 5 juillet 2018 - 14:25:08
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LIGM_A3SI | ENPC | LIGM | ENPC-LIGM | PARISTECH | LIGM_SIGNAL | UNIV-MLV | UPEC-UPEM | ESIEE-PARIS

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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〉

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