Analysis of non-local image denoising methods

Highlights? In this work we analyze non-local denoising methods. ? We connect non-local means with spectral clustering. ? We automatically estimate the parameters of non-local means. Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non-local denoising was introduced. The non-local means method proposed by Buades, Morel and Coll computes the denoised image as a weighted average of pixels across the whole image. The weight between pixels is based on the similarity between neighborhoods around them. This method attracted the attention of other researchers who proposed improvements and modifications to it. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral properties of the graph that represents the similarity of neighborhoods of the image. We also propose a method to automatically estimate the parameters which produce the optimal results in terms of mean square error and perceptual quality.