Skip to main content
Log in

Reconstruction of Sparse Signals in Impulsive Disturbance Environments

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Sparse signals corrupted by impulsive disturbances are considered. The assumption about disturbances is that they degrade the original signal sparsity. No assumption about their statistical behavior or range of values is made. In the first part of the paper, it is assumed that some uncorrupted signal samples exist. A criterion for selection of corrupted signal samples is proposed. It is based on the analysis of the first step of a gradient-based iterative algorithm used in the signal reconstruction. An iterative extension of the original criterion is introduced to enhance its selection property. Based on this criterion, the corrupted signal samples are efficiently removed. Then, the compressive sensing theory-based reconstruction methods are used for signal recovery, along with an appropriately defined criterion to detect a full recovery event among different realizations. In the second part of the paper, a case when all signal samples are corrupted by an impulsive disturbance is considered as well. Based on the defined criterion, the most heavily corrupted samples are removed. The presented criterion and the reconstruction algorithm are applied on the signal with a Gaussian noise.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. M.S. Ahmad, O. Kukrer, A. Hocanin, Robust recursive inverse adaptive algorithm in impulsive noise. Circuits Syst. Signal Process. 31(2), 703–710 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. R.G. Baraniuk. Compressive sensing. IEEE Signal Process. Mag. 24(4), 118–124 (2007)

  3. T. Blumensath, Sampling and reconstructing signals from a union of linear subspaces. IEEE Trans. Inf. Theory 57(7), 4660–4671 (2011)

    Article  MathSciNet  Google Scholar 

  4. E.J. Candès, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. E.J. Candès, T. Tao, Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  6. E.J. Candès, T. Tao, Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52(12), 5406–5425 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  7. R.E. Carrillo, K.E. Barner, T.C. Aysal, Robust sampling and reconstruction methods for sparse signals in the presence of impulsive noise. IEEE J. Sel. Top. Signal Process. 4(2), 392–408 (2010)

    Article  Google Scholar 

  8. F. Dadouchi, C. Gervaise, C. Ioana, J. Huillery, J.I. Mars, Automated segmentation of linear time-frequency representations of marine-mammal sounds. J. Acoust. Soc. Am. 134(3), 2546–2555 (2013)

    Article  Google Scholar 

  9. I. Daubechies, M. Defrise, C. De Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  10. G. Davis, S. Mallat, M. Avellaneda, Adaptive greedy approximations. Constr. Approx. 13(1), 57–98 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  11. I. Djurović, V.V. Lukin, M. Simeunović, B. Barkat, Quasi maximum likelihood estimator of polynomial phase signals for compressed sensed data. AEU Int. J. Electron. Commun. 68(7), 631–636 (2014)

    Article  Google Scholar 

  12. D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  13. D.L. Donoho, M. Elad, V.N. Temlyakov, Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans. Inf. Theory 52(1), 6–18 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. M.J. Fadili, J.L. Starck, F. Murtagh, Inpainting and zooming using sparse representations. Comput. J. 52(1), 64–79 (2009)

    Article  Google Scholar 

  15. M.A. Figueiredo, R.D. Nowak, S.J. Wright, Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007)

    Article  Google Scholar 

  16. P. Flandrin, P. Borgnat, Time-frequency energy distributions meet compressed sensing. IEEE Trans. Signal Process. 58(6), 2974–2982 (2010)

    Article  MathSciNet  Google Scholar 

  17. S.G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    Article  MATH  Google Scholar 

  18. J.J. Moré, G. Toraldo, On the solution of large quadratic programming problems with bound constraints. SIAM J. Optim. 1(1), 93–113 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  19. D. Needell, J.A. Tropp, CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmonic Anal. 26(3), 301–321 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. Z.M. Ramadan, Efficient restoration method for images corrupted with impulse noise. Circuits Syst. Signal Process. 31(4), 1397–1406 (2012)

    Article  MathSciNet  Google Scholar 

  21. A.A. Roenko, V.V. Lukin, I. Djurović, Two approaches to adaptation of sample myriad to characteristics of S\(\alpha \)S distribution data. Signal Process. 90(7), 2113–2123 (2010)

    Article  MATH  Google Scholar 

  22. E. Sejdić, A. Cam, L. Chaparro, C. Steele, T. Chau, Compressive sampling of swallowing accelerometry signals using time-frequency dictionaries based on modulated discrete prolate spheroidal sequences. EURASIP J. Adv. Signal Process. 101, 1–14 (2012)

    Google Scholar 

  23. T. Serafini, G. Zanghirati, L. Zanni, Gradient projection methods for quadratic programs and applications in training support vector machines. Optim. Methods Softw. 20(2–3), 353–378 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. I. Stanković. Recovery of images with missing pixels using a gradient compressive sensing algorithm. arXiv preprint arXiv:1407.3695 (2014)

  25. L. Stanković, A measure of some time–frequency distributions concentration. Signal Process. 81(3), 621–631 (2001)

    Article  MATH  Google Scholar 

  26. L. Stanković, M. Daković, On the reconstruction of randomly sampled sparse signals using an adaptive gradient algorithm. arxiv:1412.0624

  27. L. Stanković, M. Daković. On the uniqueness of the sparse signals reconstruction based on the missing samples variation analysis. Math. Probl. Eng. Article ID 629759 (2015). doi:10.1155/2015/629759

  28. L. Stanković, M. Daković, S. Vujović, Adaptive variable step algorithm for missing samples recovery in sparse signals. IET Signal Process. 8(3), 246–256 (2014)

    Article  Google Scholar 

  29. L. Stanković, Digital Signal Processing with Selected Topics (CreateSpace Independent Publishing Platform, An Amazon.com Company, North Charleston, 2015)

  30. L. Stanković, I. Orović, S. Stanković, M.G. Amin, Compressive sensing based separation of nonstationary and stationary signals overlapping in time–frequency. IEEE Trans. Signal Process. 61(18), 4562–4572 (2013). doi:10.1109/TSP.2013.2271752

    Article  MathSciNet  Google Scholar 

  31. L. Stanković, S. Stanković, M.G. Amin, Missing samples analysis in signals for applications to L-estimation and compressive sensing. Signal Process. 94, 401–408 (2014)

    Article  Google Scholar 

  32. L. Stanković, S. Stanković, I. Orović, M.G. Amin, Robust time-frequency analysis based on the L-estimation and compressive sensing. IEEE Signal Process. Lett. 20(5), 499–502 (2013)

    Article  Google Scholar 

  33. C. Studer, P. Kuppinger, G. Pope, H. Bolcskei, Recovery of sparsely corrupted signals. IEEE Trans. Inf. Theory 58(5), 3115–3130 (2012)

    Article  MathSciNet  Google Scholar 

  34. B.A. Turlach, On algorithms for solving least squares problems under an L1 penalty or an L1 constraint. in 2004 Proceedings of the American Statistical Association, Statistical Computing Section [CD-ROM], pp. 2572–2577 (2005)

  35. S.V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction (Wiley, Hoboken, 2008)

    Book  Google Scholar 

  36. S.J. Wright, Implementing proximal point methods for linear programming. J. Optim. Theory Appl. 65(3), 531–554 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  37. G. You, T. Qiu, A. Song, Novel direction findings for cyclostationary signals in impulsive noise environments. Circuits Syst. Signal Process. 32(6), 2939–2956 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ljubiša Stanković.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stanković, L., Daković, M. & Vujović, S. Reconstruction of Sparse Signals in Impulsive Disturbance Environments. Circuits Syst Signal Process 36, 767–794 (2017). https://doi.org/10.1007/s00034-016-0334-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-016-0334-3

Keywords

Navigation