Performance Improvement of Compressed Sensing Reconstruction Using Modified-AMP Algorithm
Compressed sensing (CS) is an emerging field which enables the undersampling of sparse signals rather than at the Nyquist rate. But the main computational challenge involved is in the reconstruction process as it is nonlinear in nature and the solution is obtained by solving a set of under determined linear equations. Greedy algorithms offer the solution to these kinds of problems with less computational complexity than the convex relaxations or linear programming methods. The approximate message passing algorithm offers accurate reconstruction of even the approximately sparse signals with reasonable computational intensity. In this paper, we have implemented a modified version of AMP algorithm and obtained a 50 % reduction in mean squared error and an improvement in signal-to-noise ratio.
KeywordsApproximate message passing algorithm Compressed sensing
- 2.Maleki A. Approximate message passing algorithms for compressed sensing. Ph. D. dissertation, Stanford University, 2011.Google Scholar
- 3.Donoho D. Compressed sensing. IEEE Trans Inf Theory. 2006;52(4):1289–306.Google Scholar
- 4.Subhashini S, Reddy AVS, Janarth M, Vignesh RA, Gandhiraj R, Soman KP. Compressive sensing based image acquisition and reconstruction analysis. In: IEEE international conference on green computing, communication and electrical engineering (ICGCCEE’14), by Dr. N.G.P. Institute of Technology, Coimbatore, 7–8 Mar 2014.Google Scholar
- 5.Gayathri S, Gandhiraj R. Analysis of ECG signal compression with compressed sensing method. In: International conference on advance engineering & technology (ICAET), Bengaluru, 23 Mar 2014.Google Scholar
- 6.Avinash P, Gandhiraj R, Soman KP. Spectrum sensing using compressed sensing techniques for sparse multiband signals. Int J Sci Eng Res. 2012;3(5).Google Scholar
- 11.Donoho D, Maleki A, Montanari A. Message-passing algorithms for compressed sensing. Proc. Nat Acad Sci. 2009;6(45):18914–9.Google Scholar
- 12.Blumensath T, Davies M. Iterative thresholding for sparse approximations. To appear in Journal of Fourier Analysis and Applications, special issue on sparsity, 2008.Google Scholar
- 13.Maechler P et al. VLSI design of approximate message passing for signal restoration and compressive sensing. IEEE J Emerg Sel Top Circ Syst. 2012;2(3):2012.Google Scholar
- 14.Montanari A. Graphical models concepts in compressed sensing. arXiv:1011.4328v3, Mar 2011.