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      Compressive sensing using the modified entropy functional

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      Author
      Kose, K.
      Gunay, O.
      Çetin, A. Enis
      Date
      2014-01
      Source Title
      Compressive sensing
      Print ISSN
      1051-2004
      Publisher
      Academic Press
      Volume
      24
      Pages
      63 - 70
      Language
      English
      Type
      Article
      Item 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 functional
      Projection onto convex sets
      Iterative row-action methods
      Bregman-projection
      Proximal splitting
      Permalink
      http://hdl.handle.net/11693/12922
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.dsp.2013.09.010
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      • Department of Electrical and Electronics Engineering 3524
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