title: Compressive Sensing via Nonlocal Smoothed Rank Function.
abstract: Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction.
authors: School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology, Chengdu, Sichuan, 611731, P. R. China.
authors: Ya-Ru
authors: YR
authors: Fan
authors: School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology, Chengdu, Sichuan, 611731, P. R. China.
authors: Ting-Zhu
authors: http://orcid.org/0000-0001-7766-230X
authors: TZ
authors: Huang
authors: School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, P. R. China.
authors: Jun
authors: J
authors: Liu
authors: School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology, Chengdu, Sichuan, 611731, P. R. China.
authors: Xi-Le
authors: XL
authors: Zhao
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*