Image Reconstruction Based on Compressive Sensing via CoSaMP

Abstract:

Compressive Sampling Matching Pursuit (CoSaMP) is a new iterative recovery algorithm which has splendid theoretical guarantees for convergence and delivers the same guarantees as the best optimization-based approaches. In this paper, we propose a new signal recovery framework which combines CoSaMP and Curvelet transform for better performance. In classic CoSaMP, the number of iterations is fixed. We discuss a new stopping rule to halting the algorithm in this paper. In addition, the choice of several adjustable parameters in algorithm such as the number of measurements and the sparse level of the signal also will impact the performance. So we gain above parameters via a large number of experiments. According to experiments, we determine an optimum value for the parameters to use in this application. The experiments show that the new method not only has better recovery quality and higher PSNRs, but also can achieve optimization steadily and effectively.

• Periodical: Applied Mechanics and Materials (Volumes 631-632)
• Pages: 436-440
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.631-632.436
• Online since: September 2014
• Authors: Lin Zhang*
• Keywords: Compressive Sensing, CoSaMP, Iteration, Parameter, Reconstruction

References

[1] E. Candès and M. Wakin: IEEE Signal Processing Magazine Vol. 25 (2008), pp.21-30.

[2] Y.D. Yue, J.Q. Yu, Y.Y. Wei, X. Liu and T.T. Cui: Journal Computational Information Systems Vol. 9 (2013), pp.7325-7331.

[3] Y.H. Li: Journal of Computer Application Vol. 31 (2011), pp.2714-2716, in Chinese.

[4] M. Reshma, H. Shiwani, P. Swathi: International Journal of Engineering Development and Research Vol. 3 (2014), pp.157-163.

[5] D. Needell and R. Vershynin: Greedy Signal Recovery and Uncertainty Principles, Proceeding of the Conference on Computational Imaging. San Jose, USA: SPIE, (2008), pp.1-12.

DOI: 10.1117/12.776996

[6] D. Needell and R. Vershynin: IEEE Journal of selected topics in signal processing Vol. 4 (2010), pp.310-316.

[7] D. Needell, R. Vershynin: Foundations of Computational Mathematics Vol. 9 (2009), pp.317-334.

[8] D. Needell and J.A. Tropp: Applied and Computational Harmonic Analysis Vol. 26 (2009), p.301–321.

[9] D. Needell and J.A. Tropp: Communications of the ACM Vol. 53 (2010), pp.93-100.

[10] D. Needell, J. A. Tropp and R. Vershynin: Greedy Signal Recovery Review, Proc. 42nd Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, (2008), pp.1048-1050.

DOI: 10.1109/acssc.2008.5074572

[11] L. Ni and Y. Miao: Computer Engineer and Application Vol. 28 (2004), pp.21-26, in Chinese.