An approximate L0 norm minimization algorithm for compressed sensing

ℓ<sup>0</sup> norm based signal recovery is attractive in compressed sensing as it can facilitate exact recovery of sparse signal with very high probability. Unfortunately, direct ℓ<sup>0</sup> norm minimization problem is NP-hard. This paper describes an approximate ℓ<sup>0</sup> norm algorithm for sparse representation which preserves most of the advantages of ℓ<sup>0</sup> norm. The algorithm shows attractive convergence properties, and provides remarkable performance improvement in noisy environment compared to other popular algorithms. The sparse representation algorithm presented is capable of very fast signal recovery, thereby reducing retrieval latency when handling

[1]  Javier Portilla,et al.  L0-Norm-Based Sparse Representation Through Alternate Projections , 2006, 2006 International Conference on Image Processing.

[2]  Ronald A. DeVore,et al.  Image compression through wavelet transform coding , 1992, IEEE Trans. Inf. Theory.

[3]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[4]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[5]  Wotao Yin,et al.  Iteratively reweighted algorithms for compressive sensing , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Bhaskar D. Rao,et al.  An affine scaling methodology for best basis selection , 1999, IEEE Trans. Signal Process..

[7]  Christian Jutten,et al.  Fast Sparse Representation Based on Smoothed l0 Norm , 2007, ICA.

[8]  Krishna R. Pattipati,et al.  Compressed sensing - a look beyond linear programming , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.