Principal component dictionary-based patch grouping for image denoising.

Improving denoising algorithms based on nonlocal self-similarity (NSS) to cope with increasing noise levels has become difficult. This is primarily because of difficulty in accurately grouping similar image patches solely on original spatial-domain of noisy images. To solve this problem, we propose to group similar patches on transform-domain learned from clean natural images. In this paper, we introduce a denoising algorithm comprising principal component dictionary (PCD)-based patch grouping and a low-rank approximation process. In the proposed algorithm, PCD learns from clean natural images and uses the knowledge gained to guide similar patches grouping results in noisy images. Patch grouping is directly implemented on PCD-based transform-domain. And, external knowledge and internal NSS prior are used jointly for image denoising. The results of experiments conducted indicate that the proposed denoising algorithm outperforms several state-of-the-art denoising algorithms, especially in heavy noise conditions. [ABSTRACT FROM AUTHOR]