PMPA: A patch-based multiscale products algorithm for image denoising

Abstract

Patch-based algorithms for image denoising have been widely used in recent years. Most of patch-based methods just exploit patch redundancy in spatial or frequency domain without considering inter-scale dependencies. In this paper, we propose a novel patch-based multiscale products algorithm (PMPA) for image denoising. It is based on patch similarity in spatial domain and multiscale products in wavelet domain. PMPA is divided into two stages to process the smooth areas and non smooth areas (such as edges) individually. The first stage is in the wavelet domain, then a locally adaptive window-based denoising method (LAWML) based on multiscale products is applied to process those wavelet coefficients corresponding to the non smooth areas, then obtain one initial denoised image. The second stage is in the spatial domain, then a non local means algorithm is used to process those pixels in the smooth areas to obtain another initial denoised image. The final denoised image is obtained by a weighted averaging of all common pixels in both initial denoised images. Experiments show that the proposed algorithm can have competitive performance compared with the state-of-the-art patch-based denoising algorithms for most of images.