Windowed variation kernel Wiener filter model for image denoising with edge preservation

Abstract

Due to the inherent characteristics of redundancy in natural images, sparsity and low-rank approximation can be applied to image processing tasks. However, most existing denoising algorithms based on low-rank approximation are pron to arise over-smoothing problem, resulting in a loss of details and structures. In this paper, a windowed variation kernel Wiener filter (WV-KWF) image denoising algorithm based on low-rank approximation is proposed. We first estimate the reference image of kernel Wiener filtering by using a low-rank approximation method. Then a windowed inherent variation is used to describe image local gradient information, and a shape-aware kernel function is introduced to describe image local complex structures. Finally the optimal kernel Wiener filter approach can be obtained for image denoising while preserving edges. The experimental results show that compared with the existing state-of-the-art methods, our proposed method is more competitive in maintaining image structures and removing noises both subjectively and objectively.