Alternating proximal algorithm for blind image recovery

Abstract : We consider a variational formulation of blind image recovery problems. A novel iterative proximal algorithm is proposed to solve the associated nonconvex minimization problem. Under suitable assumptions, this algorithm is shown to have better convergence properties than standard alternating minimization techniques. The objective function includes a smooth convex data fidelity term and nonsmooth convex regularization terms modeling prior information on the data and on the unknown linear degradation operator. A novelty of our approach is to bring into play recent nonsmooth analysis results. The pertinence of the proposed method is illustrated in an image restoration example.
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Communication dans un congrès
IEEE International Conference on Image Processing (ICIP 2010), Sep 2010, Honk Kong, China. pp.1673-1676, 2010
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Contributeur : Jean-Christophe Pesquet <>
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Dernière modification le : lundi 29 mai 2017 - 14:24:55
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  • HAL Id : hal-00844115, version 1

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Jérôme Bolte, Patrick Louis Combettes, Jean-Christophe Pesquet. Alternating proximal algorithm for blind image recovery. IEEE International Conference on Image Processing (ICIP 2010), Sep 2010, Honk Kong, China. pp.1673-1676, 2010. <hal-00844115>

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