Skip to main content
Log in

Combining Gradient-Based and Thresholding Methods for Improved Signal Reconstruction Performance

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Analysis of sparse signals has been attracting the attention of the research community in recent years. Several approaches for sparse signal recovery have been developed to provide accurate recovery from a small portion of available data. This paper proposes an improved combined approach for both accurate and computationally efficient signal recovery. Particularly, the proposed approach uses the benefits of the gradient-based steepest descent method (that belongs to the convex optimization group of algorithms) in combination with a specially designed thresholding method. This approach includes solutions for several commonly used sparse bases – the discrete Fourier, discrete cosine transform, and discrete Hermite transform, but can be adapted for other transformations as well. The presented theory is experimentally evaluated and supported by empirical data. Various analytic and real-world signals are used to assess the performance of the proposed algorithm. The analyses are performed for different percentages of available samples. The complexity of the presented algorithm can be seen through the analog hardware implementation presented in this paper. Additionally, the user-friendly graphical interface is developed with a belonging signal database to ease usage and testing. The interface allows users to choose various parameters and to examine the performance of the proposed tool in different scenarios and transformation bases.

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.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  1. Eldar, Y. C., Kutyniok, G. (2012). Compressed Sensing: Theory and Applications. Cambridge University Press.

  2. Eldar, Y. C. (2015). Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press.

    MATH  Google Scholar 

  3. Foucart, S., Rauhut, H. A Mathematical Introduction to Compressive Sensing. Birkhäuser: New York, NY, USA, 2013.

  4. Donoho, D. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306. https://doi.org/10.1109/TIT.2006.871582.

  5. Stanković, S., Orović, I., & Sejdić, E. (2015). Multimedia Signals and Systems: Basic and Advance Algorithms for Signal Processing. Springer-Verlag.

    Google Scholar 

  6. Choi, J. W., Shim, B., Ding, Y., Rao, B., & Kim, D. I. (2017). Compressed sensing for wireless communications: Useful tips and tricks. IEEE Communications Surveys & Tutorials, 19(3), 1527–1550. https://doi.org/10.1109/COMST.2017.2664421

    Article  Google Scholar 

  7. Stanković, S., Thayaparan, T., & Sučić, V. (2014). Compressive Sensing and Robust Transforms, Editorial. Special issue on Compressive Sensing and Robust Transforms.

    Google Scholar 

  8. Baraniuk, R. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24(4), 118–121.

    Article  Google Scholar 

  9. Rauhut, H., Schnass, K., & Vandergheynst, P. (2008). Compressed sensing and redundant dictionaries. IEEE Transactions on Information Theory, 54, 2210–2219.

    Article  MATH  MathSciNet  Google Scholar 

  10. Rani, M., Dhok, S. B., & Deshmukh, R. B. (2018). A systematic review of compressive sensing: Concepts, implementations and applications. IEEE Access, 6, 4875–4894. https://doi.org/10.1109/ACCESS.2018.2793851

    Article  Google Scholar 

  11. Candes, E. J., Wakin, M. B. (2008). An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 25(2), 21–30.

  12. Stanković, S., Papić, V., Li, X., Ioana, C. Algorithms for Compressive Sensing Signal Reconstruction with Applications. Mathematical Problems in Engineering, Editorial on Special issue, 2016.

  13. Stanković, S., Stanković, L. J., Orović, I. (2014). A Relationship between the Robust Statistics Theory and Sparse Compressive Sensed Signals Reconstruction. IET Signal Processing.

  14. Stanković, L. J., Sejdić, E., Stanković, S., Daković, M., Orović, I. (2019). A Tutorial on Sparse Signal Reconstruction and its Applications in Signal Processing. Circuits, Systems & Signal Processing, 38, 1206–1263. https://doi.org/10.1007/s00034-018-0909-2.

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

  16. Pope, G. (2008). Compressive sensing: a summary of reconstruction algorithms. Eidgenossische Technische Hochschule, Zurich, Switzerland.

  17. Elad, M. (2010). Sparse and Redudant Representations: From Theory to Applications in Signal and Image Processing. Springer.

  18. Zhang, T. (2011). Sparse Recovery with Orthogonal Matching Pursuit Under RIP. IEEE Transactions on Information Theory, 57(9), 6215–6221.

    Article  MATH  MathSciNet  Google Scholar 

  19. Stanković, L. J., Stanković, S., Amin, M. (2014). Missing Samples Analysis in Signals for Applications to L-estimation and Compressive Sensing. Signal Processing, 94, 401–408.

  20. Tropp, J. A. (2004). Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory, 50(10), 2231–2242.

    Article  MATH  MathSciNet  Google Scholar 

  21. Wang, J., Kwon, S., & Shim, B. (2012). Generalized orthogonal matching pursuit. IEEE Transactions on Signal Processing, 62(12), 6202–6216.

    Article  MATH  MathSciNet  Google Scholar 

  22. Tropp, J., & Gilbert, A. C. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53, 4655–4666.

    Article  MATH  MathSciNet  Google Scholar 

  23. Stanković, S., Orović, I., Stanković, L. J. (2014). An Automated Signal Reconstruction Method based on Analysis of Compressive Sensed Signals in Noisy Environment. Signal Processing, 104, 43–50.

  24. Boyd, P., Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press. Business & Economics, 716.

  25. Stanković, L. J., Daković, M. (2016). On a Gradient-Based Algorithm for Sparse Signal Reconstruction in the Signal/Measurements Domain. Mathematical Problems in Engineering, 2016, 11. Article ID 6212674. https://doi.org/10.1155/2016/6212674.

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

  27. Candes, E., Romberg, J. L1-Magic: Recovery of Sparse Signals Via Convex Programming, 2005. Available online: https://statweb.stanford.edu/~candes/software/l1magic/downloads/l1magic.pdf (accessed on July 2021).

  28. Orovic, I., Papic, V., Ioana, C., Li, X., & Stankovic, S. (2016). “Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations,” Mathematical Problems in Engineering. Review paper. https://doi.org/10.1155/2016/7616393

    Article  MATH  Google Scholar 

  29. Plumbley, M. D., Blumensath, T., Daudet, L., Gribonval, R., Davies, M. E. (2009). Sparse Representations in Audio and Music: from Coding to Source Separation. Proceedings of the IEEE, 98(6), 995–1005.

  30. Niedzwiecki, M., & Ciołek, M. (2013). Elimination of Impulsive Disturbances From Archive Audio Signals Using Bidirectional Processing. IEEE Transactions on Audio, Speech, and Language Processing, 21(5), 1046–1059.

    Article  Google Scholar 

  31. Ciołek, M., & Niedzwiecki, M. (2017). Detection of impulsive disturbances in archive audio signals. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 671–675.

  32. Záviška, P., Rajmic, P., Ozerov, A., & Rencker, L. (2021). A Survey and an Extensive Evaluation of Popular Audio Declipping Methods. IEEE Journal of Selected Topics in Signal Processing, 15(1), 5–24. https://doi.org/10.1109/JSTSP.2020.3042071

    Article  Google Scholar 

  33. Lieb, F., Stark, H. G. (2018). Audio inpainting: Evaluation of time-frequency representations and structured sparsity approaches. Signal Processing, 291–299.

  34. Qin, Z., Fan, J., Liu, Y., Gao, Y., & Li, G. Y. (2018). Sparse Representation for Wireless Communications: A Compressive Sensing Approach. IEEE Signal Processing Magazine, 35(3), 40–58. https://doi.org/10.1109/MSP.2018.2789521

    Article  Google Scholar 

  35. Rao, X., & Lau, V. K. (2015). Compressive sensing with prior support quality information and application to massive MIMO channel estimation with temporal correlation. IEEE Transaction on Signal Processing, 63(18), 4914–4924.

    Article  MATH  MathSciNet  Google Scholar 

  36. Choi, J. W., Shim, B., Ding, Y., Rao, B., Kim, D. I. (2017b). Compressed Sensing for Wireless Communications: Useful Tips and Tricks. IEEE Communications Surveys & Tutorials, 19(3), 1527–1550, third quarter.

  37. Zhang, Z., Jung, T. P., Makeig, S., Pi, Z., & Rao, B. D. (2014). Spatiotemporal Sparse Bayesian Learning With Applications to Compressed Sensing of Multichannel Physiological Signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(6), 1186–1197. https://doi.org/10.1109/TNSRE.2014.2319334

    Article  Google Scholar 

  38. Chien, J. T., & Hsieh, H. L. (2013). Bayesian group sparse learning for music source separation. EURASIP Journal on Audio, Speech, and Music Processing, 2013, 18. https://doi.org/10.1186/1687-4722-2013-18

    Article  Google Scholar 

  39. Stanković, S., Stanković, L. J., Orović, I. (2015b). Compressive sensing approach in the Hermite transform domain. Mathematical Problems in Engineering, 2015b, 9, Article ID 286590.

  40. Brajović, M. Orović, I., Daković, M., Stanković, S. (2018a). Compressive Sensing of Sparse Signals in the Hermite Transform Basis. IEEE Transactions on Aerospace and Electronic Systems, 54(2), 950–967.

  41. Brajović, M., Orović, I., Daković, M., & Stanković, S. (2016). Gradient-based signal reconstruction algorithm in the Hermite transform domain. Electronics Letters, 52(1), 41–43.

    Article  Google Scholar 

  42. Brajović, M., Vujović, S., Orović, I., Stanković, S. (2018b). Coefficient Tresholding in the Gradient Reconstruction Algorithm for Signals Sparse in the Hermite Transform Basis. Applications of Intelligent Systems 2018b (APPIS 2018b), Las Palmas De Gran Canaria, 8–12.

  43. Stanković, L. J., Brajović, M. (2018). Analysis of the Reconstruction of Sparse Signals in the DCT Domain Applied to Audio Signals. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(7), 1216–1231. https://doi.org/10.1109/TASLP.2018.2819819.

  44. Orović, I., Lekić, N., Beko, M., Stanković, S. (2019). An analog hardware solution for compressive sensing reconstruction using gradient-based method. EURASIP Journal on Advances in Signal Processing, 2019, Article number: 61.

  45. Lekić, N., Lakičević, M., Orović, I., & Stanković, S. (2018). Adaptive gradient-based analog hardware architecture for 2D under-sampled signals reconstruction. Microprocessors and Microsystems, 62, 72–78.

    Article  Google Scholar 

  46. Vujović, S., Draganić, A., Lakičević Žarić, M., Orović, I., Daković, M., Beko, M., Stanković, S. Sparse Analyzer Tool for Biomedical Signals. Sensors, 2020, 20(9), 2602. https://doi.org/10.3390/s20092602.

  47. Orović, I., Orlandić, M., Stanković, S., & Uskoković, Z. (2011). A Virtual Instrument for Time-Frequency Analysis of Signals with Highly Non-Stationary Instantaneous Frequency. IEEE Transactions on Instrumentation and Measurements, 60(3), 791–803.

    Article  Google Scholar 

  48. Zuković, S., Medenica, M., Draganić, A., Orović, I., Stanković, S. (2014). A Virtual Instrument for Compressive Sensing of Multimedia Signals. 56th International Symposium ELMAR, Zadar, Croatia.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maja Lakičević Žarić.

Ethics declarations

Informed Consent

Consent was obtained from all individual participants included in the study.

Conflict of Interest

Author declares that he has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Žarić, M.L., Draganić, A., Orović, I. et al. Combining Gradient-Based and Thresholding Methods for Improved Signal Reconstruction Performance. J Sign Process Syst 95, 643–656 (2023). https://doi.org/10.1007/s11265-022-01780-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-022-01780-5

Keywords

Navigation