Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

Abstract EN

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.