An Enhanced Lowrank Algorithm for Image Denoising

There are great breakthroughs in image denoising based on image self-similarity and the introduction of sparse representation and low rank theory. Some state-of-the-art image restoration techniques, including BM3D and SAIST are brought forward and applied to various vision tasks. In this paper, we propose an enhanced SAIST algorithm for image denoising. These improvements are mainly implemented in the following aspects. First, when matching similar blocks, matching results are depended on block distances which affected by noise interference. Thus DCT pre-filtering is introduced before aggregation because it can effectively suppress measurement errors of block distances. Second, the relevance of image patches which affects the singular value thresholding is not considered in sample mean. So a weighted sample mean calculation method is proposed to make the singular value thresholding more adaptive. The experimental results show that this improved algorithm achieves a better performance than the original algorithm in terms of both objective criterion and subjective visual quality.