Non-Convex Low-Rank Approximation for Image Denoising and Deblurring

Yang LEI  Zhanjie SONG  Qiwei SONG  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.5   pp.1364-1374
Publication Date: 2016/05/01
Publicized: 2016/02/04
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7307
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
low-rank approximation,  nuclear norm,  image restoration,  non-convex optimization,  

Full Text: PDF(1.6MB)>>
Buy this Article

Recovery of low-rank matrices has seen significant activity in many areas of science and engineering, motivated by theoretical results for exact reconstruction guarantees and interesting practical applications. Recently, numerous methods incorporated the nuclear norm to pursue the convexity of the optimization. However, this greatly restricts its capability and flexibility in dealing with many practical problems, where the singular values have clear physical meanings. This paper studies a generalized non-convex low-rank approximation, where the singular values are in lp-heuristic. Then specific results are derived for image restoration, including denoising and deblurring. Extensive experimental results on natural images demonstrate the improvement of the proposed method over the recent image restoration methods.