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Adaptive non-local means for multiview image denoising: Searching for the right patches via a statistical approach

We present an adaptive non-local means (NLM) denoising method for a sequence of images captured by a multiview imaging system, where direct extensions of existing single image NLM methods are incapable of producing good results. Our proposed method consists of three major components: (1) a robust joint-view distance metric to measure the similarity of patches; (2) an adaptive procedure derived from statistical properties of the estimates to determine the optimal number of patches to be used; (3) a new NLM algorithm to denoise using only a set of similar patches. Experimental results show that the proposed method is robust to disparity estimation error, out-performs existing algorithms in multiview settings, and performs competitively in video settings.