Joint image denoising using self-similarity based low-rank approximations

Abstract

The observed images are usually noisy due to data acquisition and transmission process. Therefore, image denoising is a necessary procedure prior to post-processing applications. The proposed algorithm exploits the self-similarity based low rank technique to approximate the real-world image in the multivariate analysis sense. It consists of two successive steps: adaptive dimensionality reduction of similar patch groups, and the collaborative filtering. For each target patch, the singular value decomposition (SVD) is used to factorize the similar patch group collected in a local search window by block-matching. Parallel analysis automatically selects the principal signal components by discarding the nonsignificant singular values. After the inverse SVD transform, the denoised image is reconstructed by the weighted averaging approach. Finally, the collaborative Wiener filtering is applied to further remove the noise. Experimental results show that the proposed algorithm surpasses the state-of-the-art methods in most cases.