Data-adaptive Color Image Denoising and Enhancement Using Graph-based Filtering

Image denoising methods have been rapidly advanced in past few years. Image denoising is a challenging process of suppressing unwanted noise components from an image while retaining image details as mush as possible. Motivated by the recent advances in graph signal processing, in this paper, we address image denoising and enhancement problems from a new graph-based viewpoint. In particular, non-local similar patches of each color channel are grouped into a block for which a graph-based framework is proposed to construct a novel dictionary. The proposed graph-based sparse coding results in removing unwanted high frequency noise from the image. In addition and to further improve the contrast level of the image, a novel enhancement method is proposed based on iterative graph filtering. Simulations are conducted to evaluate the performance of the proposed color image denoising and enhancement method and to compare it with that of the other existing methods. The proposed method is shown to provide significantly improved visual quality for denoised images as well as higher peak signal-to-noise-ratio values as compared to other existing methods.