PEWA: Patch-based Exponentially Weighted Aggregation for image denoising

Abstract : Patch-based methods have been widely used for noise reduction in recent years. In this paper, we propose a general statistical aggregation method which combines image patches denoised with several commonly-used algorithms. We show that weakly denoised versions of the input image obtained with standard methods, can serve to compute an efficient patch-based aggregated estimator. In our approach, we evaluate the Stein’s Unbiased Risk Estimator (SURE) of each denoised candidate image patch and use this information to compute the exponential weighted aggregation (EWA) estimator. The aggregation method is flexible enough to combine any standard denoising algorithm and has an interpretation with Gibbs distribution. The denoising algorithm (PEWA) is based on a MCMC sampling and is able to produce results that are comparable to the current state-of-the-art.
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Communication dans un congrès
NIPS - Neural Information Processing Systems, Dec 2014, Montreal, Canada. 2014, 〈http://papers.nips.cc/paper/5410-pewa-patch-based-exponentially-weighted-aggregation-for-image-denoising〉
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Charles Kervrann. PEWA: Patch-based Exponentially Weighted Aggregation for image denoising. NIPS - Neural Information Processing Systems, Dec 2014, Montreal, Canada. 2014, 〈http://papers.nips.cc/paper/5410-pewa-patch-based-exponentially-weighted-aggregation-for-image-denoising〉. 〈hal-01103358〉

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