Compressed Sensing with Prior Information via Maximizing Correlation
Information Theory
2017-05-23 v1 math.IT
Abstract
Compressed sensing (CS) with prior information concerns the problem of reconstructing a sparse signal with the aid of a similar signal which is known beforehand. We consider a new approach to integrate the prior information into CS via maximizing the correlation between the prior knowledge and the desired signal. We then present a geometric analysis for the proposed method under sub-Gaussian measurements. Our results reveal that if the prior information is good enough, then the proposed approach can improve the performance of the standard CS. Simulations are provided to verify our results.
Cite
@article{arxiv.1705.07695,
title = {Compressed Sensing with Prior Information via Maximizing Correlation},
author = {Xu Zhang and Wei Cui and Yulong Liu},
journal= {arXiv preprint arXiv:1705.07695},
year = {2017}
}
Comments
To appear in Proceedings of IEEE International Symposium on Information Theory 2017