Dual Non-Local Means: a two-stage information-theoretic filter for image denoising

Image denoise has been explored with the development of various filters used to remove or reduce random disruptions on observed data, but at the same time it preserves most of the edges and the fine details of the scene. The issue caused by the combined deterioration of the Gaussian noise succeeds the scattering through all the signal frequencies. Thus, the most effective filters for this type of noise are implemented in spatial domain. In this article, we proposed a Non-Local Means filter that combines the average of each fragment of a browser window, by using four measures - of distinct similarities - among the Gaussian densities that are estimated from the following fragments: the Kullback-Leibler divergence, the Bhattacharyya distance, the Hellinger distance and the Cauchy-Schwarz divergence. Computational experiments were done in a set of 7 images that were deteriorated by a noise of Gaussian type, considering that the data obtained show that the proposed methods are capable of producing, on average, a Peak Signal-to-Noise Ratio significantly greater than the one the combination of Total Variation, Non-Local Means, BM3D, Anisotropic Diffusion, Wiener, Wavelet e Bilateral filters does when they are applied independently.