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• W2110492881 endingPage "1289" @default.
• W2110492881 startingPage "1277" @default.
• W2110492881 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, peak signal-to-noise ratio value and the method noise, the proposed algorithm outperforms state-of-the-art methods." @default.
• W2110492881 created "2016-06-24" @default.
• W2110492881 creator A5010501561 @default.
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• W2110492881 date "2013-07-01" @default.
• W2110492881 modified "2023-10-18" @default.
• W2110492881 title "A Dictionary Learning Approach for Poisson Image Deblurring" @default.
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• W2110492881 doi "https://doi.org/10.1109/tmi.2013.2255883" @default.
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