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Journal of Applied Sciences >

2014 , Vol. 32 >Issue 2: 163 - 169

DOI: https://doi.org/10.3969/j.issn.0255-8297.2014.02.008

Signal and Information Processing

Atom Set Calibration and Step Control for Unknown-Sparsity Reconstruction from Compressive Sensing

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  • 1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
    2. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China

Received date: 2012-09-10

  Revised date: 2013-06-25

  Online published: 2013-06-25

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Abstract

This paper models residual signals with Gaussian-like distributions, based on which consistency between the Backtracking-based adaptive orthogonal matching pursuit (BAOMP) threshold and signal sparselevel is analyzed. An improved BAOMP (IBAOMP) method is thenproposed. Themethod estimates the
preliminary matching state usingthe 80-20 rule, and introduces a threshold with variable step size to subtly adjust atom set to raise the correct rate of selected atoms and avoid quasi-periodic mismatches of residual signals. Simulation results of 500 tests show that the exact recovery probability of IBAOMP is 17%-26% higher than BAOMP for Gaussian sparse signals, and more than70% higher than BAOMP for natural images.

Key words： compressive sensing (CS); backtracking orthogonal matching pursuit (OMP); matching set calibration; incremental step control

Cite this article

ZENG Chun-yan1,2, MA Li-hong1, DU Ming-hui1 . Atom Set Calibration and Step Control for Unknown-Sparsity Reconstruction from Compressive Sensing[J]. Journal of Applied Sciences, 2014 , 32(2) : 163 -169 . DOI: 10.3969/j.issn.0255-8297.2014.02.008

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