PEWA: Patch-based Exponentially Weighted Aggregation for image denoising
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.
This paper proposes a general aggregation method for image denoising by combining multiple weakly denoised images from standard methods. The proposed method is based on EWA and it employs Monte-Carlo simulation for efficient approximation. The results are comparable to the state-of-the-art algorithms.It is nice for the authors to provide a thorough review on the image denoising literature and to discusses the difference of PEWA with existing denoising algorithms in the beginning. As far as I understand, the main contributions of this paper (Sec 4) compared to other "internal" methods are that PEWA generate candidate patches from weakly denoised images and that it merges candidate patches based on EWA. The aggregation method is flexible enough to combine any standard denoising algorithm and has a nice interpretation with Gibbs distribution. The experiments show that the aggregated image can be significantly better than each component, and good results can be obtained efficiently.I have a concern on the flexibility of the proposed method. The authors criticize the "external" methods for not being flexible for unknown noise level in line 55-57. But PEWA also relies on a known noise level. What if the noise variance is unknown? And what if the noise is not iid Gaussian?I'm also worried about the fact that when combining a large number of denoised images, the quality will drop (line 244-246). Would that suggest the SURE is not good enough to combine a large number of patches, or the MCMC approach is not mixing well?In the experiment section, PEWA is shown to perform just similar to and sometimes worse than the state-of-the-art. It would be useful to discuss any advantage of PEWA compared to BM3D and NL-Bayes.