Compressive sensing using the modified entropy functional

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Jan 2014 Turkey English Publisher:Elsevier BVJournal:Digital Signal Processing, volume 24, pages 63-70 (issn: 1051-2004,

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Authors: Kivanç Köse; Osman Gunay; A. Enis Çetin;

doi: 10.1016/j.dsp.2013.09.010

handle: 11693/12922

Compressive sensing using the modified entropy functional

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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.

Country

Turkey

Related Organizations

Memorial Sloan Kettering Cancer Center
United States
Bilkent University
Turkey

Keywords

Compressive Sensing, Modified Entropy Functional, Iterative row-action methods, Proximal splitting, Bregman-projection Proximal Splitting, Projection Onto Convex Sets, Iterative Row-action Methods, Bregman-projection, Projection onto convex sets, Modified entropy functional

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