Markovian clustering for the non-local means image denoising

The non-local means filter is one of powerful denoising methods which allows participation of far, but proper pixels in the denoising process. Although the weights of non-similar pixels are very small, high number of these pixels results in introduction of blur. In this work, we introduce an automatic and robust method to select the best candidate pixels based on their similarity to the target pixel. This method is based on graphs partitioning and uses Markovian clustering on the pixel adjacency graph (PAG). In this way, a set of relevant pixels is obtained that is used in weighted averaging for denoising each pixel. To evaluate the method, denoising of the natural images is conducted, and the results are compared to the standard NLM filter and the SVD-based method. The results are promising.