Fast Hartley transform (FHT)based reconstruction algorithm of compressed sensing

Based on the principle of compressed sensing theory and one of the reconstruction way—Matching Pursuit (MP) algorithm, this paper improve the MP algorithm, this method analyses the time-frequency parameter property of the Gabor atom and converts the inner product calculations in MP algorithm into cross-correlation calculations to reduce the computation cost. For real signal, cross-correlation calculation can be fast done by fast Hartley transform. Test shows that this approach can greatly improve the speed of reconstruction algorithm of compressed sensing

[1]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[2]  R. Hartley A More Symmetrical Fourier Analysis Applied to Transmission Problems , 1942, Proceedings of the IRE.

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

[4]  Philipos C. Loizou,et al.  Voiced/unvoiced speech discrimination in noise using Gabor atomic decomposition , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[5]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[6]  Yaakov Tsaig,et al.  Breakdown of equivalence between the minimal l1-norm solution and the sparsest solution , 2006, Signal Process..

[7]  Douglas L. Jones,et al.  On computing the discrete Hartley transform , 1985, IEEE Trans. Acoust. Speech Signal Process..

[8]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[9]  R. Bracewell The fast Hartley transform , 1984, Proceedings of the IEEE.

[10]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.