A Dictionary Learning Approach for Poisson Image Deblurring

Abstract : The restoration of images corrupted by blur and Poisson noise is a key issue in medical and biological image processing. While most existing methods are based on variational models, generally derived from a Maximum A Posteriori (MAP) formulation, recently sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, we propose in this paper a model containing three terms: a patch-based sparse representation prior over a learned dictionary, the pixel-based total variation regularization term and a data-fidelity term capturing the statistics of Poisson noise. The resulting optimization problem can be solved by an alternating minimization technique combined with variable splitting. Extensive experimental results suggest that in terms of visual quality, PSNR value and the method noise, the proposed algorithm outperforms state-of-the-art methods.
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IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2013, 32 (7), pp.1277-1289. <10.1109/TMI.2013.2255883>
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Contributeur : Lionel Moisan <>
Soumis le : vendredi 17 février 2017 - 00:58:22
Dernière modification le : jeudi 23 février 2017 - 01:08:27

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Liyan Ma, Lionel Moisan, Jian Yu, Tieyong Zeng. A Dictionary Learning Approach for Poisson Image Deblurring. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2013, 32 (7), pp.1277-1289. <10.1109/TMI.2013.2255883>. <hal-00834517v2>

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