Non-local means denoising using a content-based search region and dissimilarity kernel

The Non-Local Means (NLM) denoising algorithm uses a weighted average of pixels, within a defined search region in an image, to estimate the noise-free pixel value. The search region is usually a rectangular neighborhood, centered at the pixel of interest, which may include pixels whose original gray value do not match the value of the original central pixel. Consequently, their participation in the averaging process degrades denoising performance. To eliminate their effect, researchers suggest creating an adaptive search region which excludes those dissimilar pixels. In this paper, we present a novel model-based method which defines a set of similar pixels, from the initial search region, using the statistical distribution of the dissimilarity measure. Moreover, to enhance the denoising, our method also adaptively assigns one of two dissimilarity kernels to each pixel, based on its local features. Experimental results show that the proposed algorithm has better performance than the original one in terms of PSNR, SSIM, and visual quality and is found to be more efficient than other examined approaches.