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Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

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  • ZheTao Li
  • JingXiong Xie
  • DengBiao Tu
  • Young-June Choi

Abstract

In this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate through threshold processing at first and then recovers the signal through the largest correlation coefficients. We demonstrate that SSP significantly outperforms conventional techniques in recovering sparse signals whose nonzero values have exponentially decaying magnitudes or distribution of N ( 0,1 ) . Experimental results of Lena show that SSP is better than CoSaMP, OMP, and SP in terms of peak signal to noise ratio (PSNR) by 5.5 db, 4.1 db, and 4.2 db, respectively.

Suggested Citation

  • ZheTao Li & JingXiong Xie & DengBiao Tu & Young-June Choi, 2013. "Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing," International Journal of Distributed Sensor Networks, , vol. 9(8), pages 798537-7985, August.
    • Handle: RePEc:sae:intdis:v:9:y:2013:i:8:p:798537
    DOI: 10.1155/2013/798537
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