Open AccessImproved RPCA method via non‐convex regularisation for image denoising
Author(s) -
Wang Sijie,
Xia Kewen,
Wang Li,
Zhang Jiangnan,
Yang Huaijin
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2019.0365
Subject(s) - robust principal component analysis , matrix norm , singular value , noise reduction , computer science , norm (philosophy) , image restoration , algorithm , mathematical optimization , penalty method , mathematics , artificial intelligence , pattern recognition (psychology) , image (mathematics) , principal component analysis , image processing , eigenvalues and eigenvectors , physics , quantum mechanics , political science , law
The traditional robust principal component analysis (RPCA) model is based on the nuclear norm, which usually underestimates the singular values of the low‐rank matrix. As a consequence, the restoration image experiences serious interference by Gaussian noise, and the image quality degenerates during the denoising process. Therefore, an improved RPCA method via non‐convex regularisation terms is proposed to remedy the above shortcomings. First, in order to estimate the singular value of the low‐rank matrix more accurately, the authors employ the non‐convex penalty function and add a weight vector to it. Then, the regularisation with non‐convex penalty function and its weighted version are used to replace the nuclear norm and entry‐wise l 1 norm in original RPCA, respectively, to establish an improved model. Finally, an optimal solution algorithm is derived by developing the alternating direction multiplier method. Experimental results show that the proposed method has better performance in terms of both quantitative measurement and visual perception quality than other several state‐of‐the‐art image denoising methods.