IMPROVED MEASURE ALGORITHM BASED ON CoSaMP FOR IMAGE RECOVERY

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 7 , ISSUE 2 (June 2014) > List of articles

IMPROVED MEASURE ALGORITHM BASED ON CoSaMP FOR IMAGE RECOVERY

Guohui Wu * / Xingkun Li * / Jiyang Dai *

Keywords : Compressed sensing, Measurement Matrix, Fourier Ring, Orthogonal Matching.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 2, Pages 724-739, DOI: https://doi.org/10.21307/ijssis-2017-678

License : (CC BY-NC-ND 4.0)

Received Date : 06-February-2014 / Accepted: 25-April-2014 / Published Online: 27-December-2017

ARTICLE

ABSTRACT

In order to improve the quality of the reconstruction image which using Compressive sensing(CS) algorithm. Based on improved measurement matrix combined with CS Matching Pursuit(CoSaMP)algorithm, this paper presents a kind of Fourier Ring Compressive Sampling Matching Pursuit (FR-CoSaMP) algorithm. The algorithm superimposed deterministic ring measurement matrix to optimize measurement process on the basis of Fourier measurement matrix. And solve the iterative inverse operation by using FFT fast Fourier calculation method, which can make the measurement information more complete, and speed up the signal reconstruction. Then introduces the mathematical framework and algorithmic processes of the FR-CoSaMP algorithm in details. Finally, compare these types of traditional algorithms and the improved algorithm by analysis and simulation. The results show that, under the same image sparsity and measurement scale, the improved FR-CoSaMP algorithm has better performance in terms of the image reconstruction.

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REFERENCES

[1] Donoho, D. L. Compressed sensing. Information Theory, IEEE Transactions on, 52(4), pp,1289-1306,2006.
[2] Candès E,Romberg J,Tao T. Robust uncertainty principles:exact signal recognition from highly incomplete frequency information[J]. IEEE Trans.Info.Theory,52(2), pp.489-509, 2006.
[3] Lee S R. A Coarse-to-Fine Approach for Remote-Sensing Image Registration Based on a Local Method[J]. International Journal on Smart Sensing & Intelligent Systems, 3(4), 2010.
[4] Condat, L.. A primal–dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms. Journal of Optimization Theory and Applications, 158(2), 460-479,2013.
[5] Harmany, Z., Thompson, D., Willett, R., & Marcia, R. F. (2010, September). Gradient projection for linearly constrained convex optimization in sparse signal recovery. In Image Processing (ICIP), 2010 17th IEEE International Conference on (pp. 3361-3364). IEEE.
[6] Iwen, M. A. (2009, March). Simple deterministically constructible rip matrices with sublinear fourier sampling requirements. In Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on (pp. 870-875). IEEE.
[7] Zhang, T. Sparse recovery with orthogonal matching pursuit under RIP. Information Theory, IEEE Transactions on, 57(9),pp. 6215-6221,2011.
[8] Zhang, Y. Theory of Compressive Sensing via ℓ 1-Minimization: a Non-RIP Analysis and Extensions. Journal of the Operations Research Society of China, 1(1), pp.79-105,2013.
[9] Plan, Y., & Vershynin, R. One‐Bit Compressed Sensing by Linear Programming. Communications on Pure and Applied Mathematics, 66(8),pp. 1275-1297,2013.
[10] Patel, V. M., Maleh, R., Gilbert, A. C., & Chellappa, R. Gradient-based image recovery methods from incomplete fourier measurements. Image Processing, IEEE Transactions on, 21(1), 94-105,2013.
[11] Do, T. T., Gan, L., Nguyen, N. H., & Tran, T. D. Fast and efficient compressive sensing using structurally random matrices. Signal Processing, IEEE Transactions on, 60(1), pp.139-154,2012.
[12] Lechner B, Lieschnegg M, Mariani O, et al. A WAVELET-BASED BRIDGE WEIGH-IN-MOTION SYSTEM[J]. International Journal on Smart Sensing & Intelligent Systems, 3(4),2010.
[13] Needell, D., & Tropp, J. A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 26(3),pp. 301-321,2009.
[14] Zhang, L., Xing, M., Qiu, C. W., Li, J., Sheng, J., Li, Y., & Bao, Z.. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing. Geoscience and Remote Sensing, IEEE Transactions on, 48(10),pp. 3824-3838,2010.
[15] Yussof H, Wada J, Ohka M. ANALYSIS OF TACTILE SLIPPAGE CONTROL ALGORITHM FOR ROBOTIC HAND PERFORMING GRASP-MOVE-TWIST MOTIONS[J]. International Journal on Smart Sensing & Intelligent Systems, 3(3) ,2010.
[16] Wang, J., & Shim, B. On the recovery limit of sparse signals using orthogonal matching pursuit. Signal Processing, IEEE Transactions on, 60(9), pp.4973-4976,2012.
[17] Candes, E. J., Eldar, Y. C., Needell, D., & Randall, P. Compressed sensing with coherent and redundant dictionaries. Applied and Computational Harmonic Analysis, 31(1),pp. 59-73,2011.
[18] Cevher, V., Boufounos, P., Baraniuk, R. G., Gilbert, A. C., & Strauss, M. J. (2009, April). Near-optimal bayesian localization via incoherence and sparsity. In Proceedings of the 2009 International Conference on Information Processing in Sensor Networks (pp. 205-216). IEEE Computer Society.
[19] Padmanabhan,K.; Dhanalakshmi, T.; Image Compression and PSNR Ratio-for an Intensity Image Using Wavelet transform, Journal of the Instrument Society of India,Vol.42 ,2012.

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