First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a denoising algorithm based on non-local image statistics and patch repetition by combining the advantages of NL-means and Exponentially Weighted Aggregation (EWA). The computation of the aggregated estimator is done using MCMC and results are comparable to state-of-the-art algorithms. Pluses: 1) the method seems simple and straightforward to implement. Minus: In terms of explaining *why* the method works, the text leaves something to be desired. E.g., in the second paragraph of section 7 (The proposed implementation proceeds in two identical iterations.)

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 estimd aggregation (EWA) estimator. The resulting approach (PEWA) is based on a MCMC sampling and has a nice statistical foundation while producing denoising results that are comparable to the current state-of-the-art. We demonstrate the performance of the denoising algorithm on real images and we compare the results to several competitive methods.

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.

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.

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 estimd aggregation (EWA) estimator. The resulting approach (PEWA) is based on a MCMC sampling and has a nice statistical foundation while producing denoising results that are comparable to the current state-of-the-art. We demonstrate the performance of the denoising algorithm on real images and we compare the results to several competitive methods.

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 imagepatch and use this information to compute the exponential weighted aggregation (EWA) estimator. The aggregation method is flexible enough to combine anystandard denoising algorithm and has an interpretation with Gibbs distribution. Thedenoising algorithm (PEWA) is based on a MCMC sampling and is able to produce results that are comparable to the current state-of-the-art.