Compressive Sensing Using the Entropy Functional
Information Theory
2015-03-18 v2 math.IT
Abstract
In most compressive sensing problems l1 norm is used during the signal reconstruction process. In this article the use of entropy functional is proposed to approximate the l1 norm. A modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman's row action D-projection method for compressive sensing applications. Simulation examples are presented.
Cite
@article{arxiv.1101.5079,
title = {Compressive Sensing Using the Entropy Functional},
author = {Kivanc Kose and Osman Gunay and A. Enis Cetin},
journal= {arXiv preprint arXiv:1101.5079},
year = {2015}
}