Effective compressive sensing via reweighted total variation and weighted nuclear norm regularization
Mingli Zhang, Christian Desrosiers, Caiming Zhang
Abstract
Total variation (TV) and non-local patch similarity have been used successfully to enhance the performance of compressive sensing (CS) approaches. However, such techniques can often remove important details in the image or introduce reconstruction artifacts. This paper presents a novel CS method, which uses an adaptive reweighted TV strategy to better preserve image edges. Our method also leverages the redundancy of non-local image patches through the use of weighted low rank regularization. An optimization strategy based on the ADMM algorithm is used to reconstruct images efficiently. Experimental results show our method to outperform state-of-the-art CS approaches, for various sampling ratios.
BibTeX
@inproceedings{icassp2017_effectivecompres,
title = {Effective compressive sensing via reweighted total variation and weighted nuclear norm regularization},
author = {Mingli Zhang and Christian Desrosiers and Caiming Zhang},
booktitle = {ICASSP 2017},
year = {2017}
}