Kernel Wiener filtering model with low-rank approximation for image denoising

Sparse representation and low-rank approximation have recently attracted great interest in the field of image denoising. However, they have limited ability for recovering complex image structures due to the lack of satisfactory local image descriptors and shrinkage rules of transformed coefficients, especially for degraded images with heavy noise. In this paper, we propose a novel kernel Wiener filtering model with low-rank approximation for image denoising. In the model, a shape-aware kernel function is introduced to describe local complex image structures. The reference image of kernel Wiener filtering is estimated by an optimized low-rank approximation approach, where eigenvalue thresholding is deduced for the shrinkage of transformed coefficients using a prior nonlocal self-similarity. Finally the optimal kernel Wiener filter is derived for image noise reduction. Our experimental results show that the proposed model can faithfully restore detailed image structures while removing noise effectively, and often outperforms the state-of-the-art methods both subjectively and objectively.