Compressive sensing using the modified entropy functional
Author
Kose, K.
Gunay, O.
Çetin, A. Enis
Date
2014-01Source Title
Compressive sensing
Print ISSN
1051-2004
Publisher
Academic Press
Volume
24
Pages
63 - 70
Language
English
Type
ArticleItem Usage Stats
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Abstract
In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In
this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The
proposed 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 method for
compressive sensing applications. Simulation examples with both 1D signals and images are presented.
© 2013 Elsevier Inc. All rights reserved.
Keywords
Modified entropy functionalProjection onto convex sets
Iterative row-action methods
Bregman-projection
Proximal splitting