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.