Skip to main content
Log in

Improved adaptive forward-backward matching pursuit algorithm to compressed sensing signal recovery

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

As a novel two-stage greedy approximation algorithm, Forward-Backward Pursuit (FBP) algorithm attracts wide attention because of its high reconstruction accuracy and no need for sparsity as a priori information. However, the FBP algorithm has to spend much more time to get a higher accuracy. In view of this, an Improved Adaptive Forward-Backward Matching Pursuit (IAFBP) algorithm is proposed in this paper. In the forward stage, the IAFBP algorithm uses an adaptive threshold to select the appropriate number of atoms into support set, so that the number of selected atoms is more random. In the backward stage, the projection coefficient of the atoms is taken as the basis of rejection, and the deletion threshold is introduced to reject the atoms adaptively, so that more right atoms are retained in each iteration and the reconstruction speed can be accelerated. At the same time, it overcomes the excessive backtracking phenomenon existing in the adaptive process and improves the accuracy of the algorithm. The simulation results of one-dimensional sparse signals and two-dimensional images show that the IAFBP algorithm has more advantages than the FBP algorithm in reconstruction performance and computational time.

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

Similar content being viewed by others

References

  1. Candes EJ, TAO T (2006) Near optimal signal recovery from random projections:universal encoding strategies. IEEE Trans Inform Theory 52(12):5406–5425

    Article  MathSciNet  Google Scholar 

  2. Candes EJ, Eldar YC, Needell D et al (2011) Compressed sensing with coherent and redundant dictionaries. Appl Comput Harmon Anal 31(1):59–73

    Article  MathSciNet  Google Scholar 

  3. Donoho DL (2006) Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306

    Article  MathSciNet  Google Scholar 

  4. Fan C, Wang L, Liu P (2016) Compressed sensing based remote sensing image reconstruction via employing similarities of reference images. Multimed Tools Appl 77(19):12201–12225

    Article  Google Scholar 

  5. Guo H, Han S, Hao F et al (2017) SOSP: a stepwise optimal sparsity pursuit algorithm for practical compressed sensing. Multimed Tools Appl 77(7):1–24

    Google Scholar 

  6. Karahanoglu NB, Erdogan H (2010) A* orthogonal matching pursuit: best-first search for compressed sensing signal recovery. Digit Signal Process 22(4):555–568

    Article  MathSciNet  Google Scholar 

  7. Karahanoglu NB, Erdogan H (2013) Compressed sensing signal recovery via forward backward pursuit. Digit Signal Process 23(5):1539–1548

    Article  MathSciNet  Google Scholar 

  8. Karahanoğlu NB, Erdoğan H (2013) Optimal forward-backward pursuit for the sparse signal recovery problem. Proceeding of 21th signal processing and communications applications conference (SIU), 21–24

  9. Lu W, Li W, Kpalma K et al (2015) Compressed sensing performance of random Bernoulli matrices with high compression ration. IEEE Signal Process Lett 22(8):1074–1078

    Article  Google Scholar 

  10. Mallat SG, Zhang ZF (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415

    Article  Google Scholar 

  11. Meng X, Zhao R, Cen Y (2016) A modified regularized adaptive matching pursuit algorithm for compressed sampling signal reconstruction. Signal Process 32(2):186–192

    Google Scholar 

  12. Meng Z, Pan Z, Li J et al (2019) Improved backtracking regularized adaptive matching pursuit algorithm and its application. Chinese High Technol Lett 29(02):110–118

    Google Scholar 

  13. Needell D, Vershynin R (2009) Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Found Comput Math 9(3):317–334

    Article  MathSciNet  Google Scholar 

  14. Peng Y, He Y, Lin B (2012) Noise signal recovery algorithm based on singular value decomposition incompressed sensing. Chin J Sci Instrum 33(12):2655–2660

    Google Scholar 

  15. Qaisar S, Bilal RM, Lqbal W (2013) Compressive sensing: from theory to applications, a survey. J Commun Netw 15(5):443–456

    Article  Google Scholar 

  16. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inform Theory 53(12):4655–4666

    Article  MathSciNet  Google Scholar 

  17. Wang W, Wu R (2013) High resolution direction of arrival (DOA) estimation based on improved orthogonal matching pursuit (OMP) algorithm by iterative local searching. Sensors 13(9):11167–11183

    Article  Google Scholar 

  18. Wang L, Zhou L, Ji H et al (2014) A new matching pursuit algorithm for signal classification. J Electron Inform Technol 36(6):1299–1306

    Google Scholar 

  19. Wang F, Sun G, Zhang J (2016) Acceleration forward-backward pursuit algorithm based on compressed sensing. J Electron Inform Technol 38(10):2538–2545

    Google Scholar 

  20. Yan C, Li L, Zhang C, et al (2019) Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans Multimedia, to be published

  21. Yao S, Wang T, Shen W (2015) Research of incoherence rotated chaotic measurement matrix in compressed sensing. Multimed Tools Appl 76(17):1–19

    Google Scholar 

  22. Zhang Y, Qi R, Zeng Y (2017) Forward-backward pursuit method for distributed compressed sensing. Multimed Tools Appl 76(20):20587–20608

    Article  Google Scholar 

  23. Zheng S, Chen J, Kuo Y (2018) An improved distributed compressed video sensing scheme in reconstruction algorithm. Multimed Tools Appl 77(7):8711–8728

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 51575472), by the Natural Science Foundation of Hebei Province of China (No.E2019203448), the scientific research program of Hebei Education Department (No. ZD2015049) and the scientific research program for Talents Returning from Overseas of Hebei Province (No. C2015005020).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zong Meng.

Ethics declarations

Conflict of interest

The authors declare 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

Meng, Z., Pan, Z., Shi, Y. et al. Improved adaptive forward-backward matching pursuit algorithm to compressed sensing signal recovery. Multimed Tools Appl 78, 33969–33984 (2019). https://doi.org/10.1007/s11042-019-08161-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08161-4

Keywords

Navigation