Open AccessCompressive Sensing via Nonlocal Smoothed Rank Function
Author(s) -
Yaru Fan,
TingZhu Huang,
Jun Li,
Xi-Le Zhao
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0162041
Subject(s) - compressed sensing , rank (graph theory) , similarity (geometry) , function (biology) , image (mathematics) , computer science , regular polygon , algorithm , minification , convex function , mathematical optimization , mathematics , artificial intelligence , combinatorics , geometry , evolutionary biology , biology
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.