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A Two-Stage Fusing Method of Reconstruction Algorithms for Compressed Sensing

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Abstract

There are still many algorithms proposed recently to reconstruct signals in Compressed Sensing (CS) setup. However, how to reconstruct sparse signals accurately with fewer measurements and less time is still a problem. It is interesting to observe that algorithms with poor performance do not mean a complete failure, as their support set may include some correct indices that some algorithms with good performance may not find out. Because of this, people proposed some fusing method using modified algorithms and partial support set, however, the reliability of the set is the key to the algorithm, and the modified method of different algorithms and the reconstruction performances of different modified algorithms are still needed to be verified. In this paper, we propose a two-stage fusing method for Compressed Sensing algorithms. From existing algorithms, we choose one as the main algorithm, some other as prior algorithms and run them in different stages. In the first stage we get high-accuracy atomic set from the prior algorithms and in the second stage we use the atomic set as the partial support set and fuse it with the main algorithm adaptively to improve the sparse signal reconstruction. The proposed method is suitable for most CS algorithms which work with different principles. According to the simulation results, the proposed method improves the performance of participating algorithms and is superior to other fusing methods in both reconstruction accuracy and reconstruction time.

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References

  1. D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, Vol. 52, pp. 1289–1306, 2006.

    Article  MathSciNet  Google Scholar 

  2. E. J. Candes and T. Tao, Near-optimal signal recovery from random projections: Universal encoding strategies?, IEEE Transactions on Information Theory, Vol. 52, pp. 5406–5425, 2006.

    Article  MathSciNet  Google Scholar 

  3. E. P. K. Gilbert, B. Kaliaperumal, E. B. Rajsingh, and M. Lydia, Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks, Computers & Electrical Engineering, 2018.

  4. S. Xiao, T. Li, Y. Yan, et al., Cluster Comput, 2018. https://doi.org/10.1007/s10586-018-2259-z.

    Article  Google Scholar 

  5. A. Maleki and D. L. Donoho, Optimally tuned iterative reconstruction algorithms for compressed sensing, IEEE Journal of Selected Topics in Signal Processing, Vol. 4, pp. 330–341, 2010.

    Article  Google Scholar 

  6. B. L. Sturm, Sparse vector distributions and recovery from compressed sensing, Computer Science, Vol. abs/1103.6246, 2011.

  7. J. A. Tropp and A. C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory, Vol. 53, pp. 4655–4666, 2007.

    Article  MathSciNet  Google Scholar 

  8. M. A. Lodhi, S. Voronin, and W. U. Bajwa, YAMPA: Yet another matching pursuit algorithm for compressive sensing. In SPIE Commercial + Scientific Sensing and Imaging, 2016.

  9. J. D. Blanchard and J. Tanner, Performance comparisons of greedy algorithms in compressed sensing, Numerical Linear Algebra with Applications, Vol. 22, pp. 254–282, 2015.

    Article  MathSciNet  Google Scholar 

  10. P. B. Swamy, S. K. Ambat, S. Chatterjee, and K. V. S. Hari, Reduced look ahead orthogonal matching pursuit. In 2014 Twentieth National Conference on Communications (Ncc), 2014.

  11. Wang F, Sun G, Li Z, et al., Fusion Forward–backward pursuit algorithm for compressed sensing, International Journal of Wireless Information Networks, pp. 1–8, 2017.

  12. Y. Xu, G. Sun, T. Geng and Z. Li, An improved method for OMP-based algorithms using fusing strategy. In 13th IEEE Colloquium on Signal Processing and its Applications (CSPA 2017), pp. 205–210 March 2017.

  13. S. K. Ambat, S. Chatterjee and K. V. S. Hari, A committee machine approach for compressed sensing signal reconstruction, IEEE Transactions on Signal Processing, Vol. 62, pp. 1705–1717, 2014.

    Article  MathSciNet  Google Scholar 

  14. S. Narayanan, S. K. Sahoo, and A. Makur, Greedy pursuits assisted basis pursuit for compressive sensing. In Signal Processing Conference (EUSIPCO), 2015 23rd European, 2015, pp. 694–698.

  15. E. J. Candes, J. Romberg and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. on Information Theory, Vol. 52, pp. 489–509, 2006.

    Article  MathSciNet  Google Scholar 

  16. S. Chatterjee, D. Sundman, and M. Skoglund, Look ahead orthogonal matching pursuit. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4024–4027, 2011.

  17. P. H. Lin, S. H. Tsai, and G. C. H. Chuang, A K-best orthogonal matching pursuit for compressive sensing. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (Icassp), pp. 5706–5709, 2013.

  18. W. Dai and O. Milenkovic, Subspace pursuit for compressive sensing signal reconstruction, Information Theory IEEE Transactions on, Vol. 55, pp. 2230–2249, 2009.

    Article  MathSciNet  Google Scholar 

  19. D. Needell and J. A. Tropp, CoSaMP: iterative signal recovery from incomplete and inaccurate samples, Applied and Computational Harmonic Analysis, Vol. 26, No. 3, pp. 301–321, 2009.

    Article  MathSciNet  Google Scholar 

  20. R. E. Carrillo, L. F. Polania, and K. E. Barner, Iterative algorithms for compressed sensing with partially known support. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3654–3657, Mar. 2010.

  21. S. Narayanan, S. K. Sahoo and A. Makur, Recovery of correlated sparse signals using adaptive backtracking matching pursuit, IEEE Visual Communication and Image Processing (VCIP), pp. 1–4, Dec. 2015.

  22. T. Blumensath and M. E. Davies, Iterative hard thresholding for compressed sensing, Applied & Computational Harmonic Analysis, Vol. 27, No. 3, pp. 265–274, 2008.

    Article  MathSciNet  Google Scholar 

  23. T. Blumensath and M. E. Davies, Normalized iterative hard thresholding: guaranteed stability and performance, Selected Topics in Signal Processing IEEE Journal of, Vol. 4, No. 2, pp. 298–309, 2010.

    Article  Google Scholar 

  24. T. Blumensath, Accelerated iterative hard thresholding, Signal Processing, Vol. 92, No. 3, pp. 752–756, 2012.

    Article  Google Scholar 

  25. R. E. Carrillo, L. F. Polania, and K. E. Barner, Iterative hard thresholding for compressed sensing with partially known support. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4028–4031, May 2011.

  26. S. S. B. Chen, D. L. Donoho and M. A. Saunders, Atomic decomposition by basis pursuit, Siam Journal on Scientific Computing, Vol. 20, pp. 33–61, 1998.

    Article  MathSciNet  Google Scholar 

  27. N. Vaswani and W. Lu, Modified-CS: modifying compressive sensing for problems with partially known support, IEEE Transactions on Signal Processing, Vol. 58, pp. 4595–4607, 2010.

    Article  MathSciNet  Google Scholar 

  28. S. Narayanan, S. K. Sahoo and A. Makur, Modified adaptive basis pursuits for recovery of correlated sparse signals. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 4136–4140, May 2014.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China (No. 61771262) and Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology.

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Authors and Affiliations

  1. College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China

    Yi Xu, Guiling Sun, Tianyu Geng & Ying Zhang

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  1. Yi Xu
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  2. Guiling Sun
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  3. Tianyu Geng
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  4. Ying Zhang
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Correspondence to Guiling Sun.

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Xu, Y., Sun, G., Geng, T. et al. A Two-Stage Fusing Method of Reconstruction Algorithms for Compressed Sensing. Int J Wireless Inf Networks 25, 480–487 (2018). https://doi.org/10.1007/s10776-018-0409-0

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  • DOI: https://doi.org/10.1007/s10776-018-0409-0

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