MRWM: A Multiple Residual Wasserstein Driven Model for Image Denoising

Abstract

Residual histograms can provide valuable information for vision research. However, current image restoration methods have not fully exploited the potential of multiple residual histograms, especially their role as overall regularization constraints. In this paper, we propose a novel framework of multiple residual Wasserstein driven model (MRWM) that can organically combine multiple residual Wasserstein constraints and various natural image priors for image denoising. Specifically, by utilizing the Wasserstein distance derived from the optimal transmission theory, the multiple residual histograms of the observed images are forced to be as close as possible to the reference residual histogram, thereby improving the accuracy of residual estimation. Furthermore, the proposed concrete MRWM unifies the multiple residual Wasserstein distribution approximation and the image total variation prior knowledge to carry out image denoising. Alternating iterative algorithm of histogram matching and Chambolle dual projection has the characteristics of less parameters and easy implementation. Finally, our experiments confirm that compared with some representative image denoising algorithms, the MRWM can obtain better performance in objective evaluation, and can better preserve the details such as the image edges, making the image look more natural.