A greedy approach to sparse poisson denoising

François-Xavier Dupé 1 Sandrine Anthoine 2, *
* Corresponding author
1 QARMA - éQuipe AppRentissage et MultimediA [Marseille]
LIF - Laboratoire d'informatique Fondamentale de Marseille
Abstract : In this paper we propose a greedy method combined with the Moreau-Yosida regularization of the Poisson likelihood in order to restore images corrupted by Poisson noise. The regularization provides us with a data fidelity term with nice properties which we minimize under sparsity constraints. To do so, we use a greedy method based on a generalization of the well-known CoSaMP algorithm. We introduce a new convergence analysis of the algorithm which extends it use outside of the usual scope of convex functions. We provide numerical experiments which show the soundness of the method compared to the convex 1 -norm relaxation of the problem.
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François-Xavier Dupé, Sandrine Anthoine. A greedy approach to sparse poisson denoising. Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on, Sep 2013, Southampton, United Kingdom. pp.1-6, ⟨10.1109/MLSP.2013.6661993⟩. ⟨hal-00998189⟩

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