Compressive sensing using the modified entropy functional

  • Authors:
  • Kivanc Kose;Osman Gunay;A. Enis Cetin

  • Affiliations:
  • Electrical and Electronics Engineering Department, Bilkent University, Turkey and Dermatology Service, Memorial Sloan-Kettering Cancer Center, USA;Electrical and Electronics Engineering Department, Bilkent University, Turkey;Electrical and Electronics Engineering Department, Bilkent University, Turkey

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2014

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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.