Skip to main content
SpringerLink
Log in

Compressed Sensing Joint Image Reconstruction Based on Multiple Measurement Vectors

  • Conference paper
  • First Online:
6GN for Future Wireless Networks (6GN 2021)

Included in the following conference series:

  • 837 Accesses

Abstract

In order to improve the quality of the reconstructed image for compressed sensing, a novel compressed sensing joint image reconstruction method based on multiple measurement vectors is put forward in this paper. Firstly, the original image is processed under the multiple measurement vectors (MWV) mode and random measured by two compressive imaging cameras, in vertical direction and horizontal direction, separately. Secondly, the vertical sampling image and horizontal sampling image are reconstructed with the multiple measurement vectors. Finally, the mean image is used to capture the correlation between these two similar images, and the original image is reconstructed. The experiment result showed that the visual effect and peak signal to noise ratio (PSNR) of the joint reconstructed image by this method is much better than the independent reconstructed images. So, it is an effective compressed sensing joint image reconstruction method.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  MathSciNet  Google Scholar 

  2. Baraniuk, R.: Compressive sensing. IEEE Sig. Process. Mag. 24(4), 118–124 (July 2007)

    Article  Google Scholar 

  3. Zhou, Y., Zeng, F.-Z., Gu, Y.-C.: A gradient descent sparse adaptive matching pursuit algorithm based on compressive sensing. In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 464–469 (2016)

    Google Scholar 

  4. Khajehnejad, M.A., Xu, W., Avestimehr, A.S., Hassibi, B.: Improving the thresholds of sparse recovery: an analysis of a two-step reweighted basis pursuit algorithm. IEEE Trans. Inf. Theory 61(9), 5116–5128 (2015)

    Article  MathSciNet  Google Scholar 

  5. Xu, F.M., Wang, S.H.: A hybrid simulated annealing thresholding algorithm for compressed sensing. Sig. Process. 93(6), 1577–1585 (June 2013)

    Article  Google Scholar 

  6. Fei, X., Li, L., Cao, H., Miao, J., Yu, R.: View’s dependency and low-rank background-guided compressed sensing for multi-view image joint reconstruction. IET Image Process. 13(12), 2294–2303 (2019)

    Article  Google Scholar 

  7. Qiao, H., Pal, P.: Guaranteed localization of more sources than sensors with finite snapshots in multiple measurement vector models using difference co-arrays. IEEE Trans. Sig. Process. 67(22), 5715–5729 (2019)

    Article  MathSciNet  Google Scholar 

  8. Wang, J., Kwon, S., Li, P., Shim, B.: Recovery of sparse signals via generalized orthogonal matching pursuit: a new analysis. IEEE Trans. Sig. Process. 64(4), 1076–1089 (2016)

    Article  MathSciNet  Google Scholar 

  9. Amel, R., Feuer, A.: Adaptive identification and recovery of jointly sparse vectors. IEEE Trans. Sig. Process. 62(2), 354–362 (2014)

    Article  MathSciNet  Google Scholar 

  10. Wen, J., Yu, W.: Exact sparse signal recovery via orthogonal matching pursuit with prior information. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, pp. 5003–5007 (2019)

    Google Scholar 

  11. Shousen, C., Quanzhu, J., Qiang, X.: Image super-resolution reconstruction based on compressed sensing. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, pp. 368–374 (2017)

    Google Scholar 

  12. Tillmann, A.M., Pfetsch, M.E.: The computational complexity of the restricted isometry property, the nullspace property, and related concepts in compressed sensing. IEEE Trans. Inf. Theory 60(2), 1248–1259 (2014)

    Article  MathSciNet  Google Scholar 

  13. Xuan, V.N., Hartmann, K., Weihs, W., Loffeld, O.: Modified orthogonal matching pursuit for multiple measurement vector with joint sparsity in super-resolution compressed sensing. In: 2017 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 840–844 (2017)

    Google Scholar 

  14. Zhu, J., Wang, J., Zhu, Q.: Compressively sensed multi-view image reconstruction using joint optimization modeling. In: 2018 IEEE Visual Communications and Image Processing (VCIP), Taichung, Taiwan, pp. 1–4 (2018)

    Google Scholar 

  15. Zhuang, S., Zhao, W., Wang, R., Wang, Q., Huang, S.: New measurement algorithm for supraharmonics based on multiple measurement vectors model and orthogonal matching pursuit. IEEE Trans. Instrum. Meas. 68(6), 1671–1679 (2019)

    Article  Google Scholar 

  16. Anselmi, N., Salucci, M., Oliveri, G., Massa, A.: Wavelet-based compressive imaging of sparse targets. IEEE Trans. Antennas Propag. 63(11), 4889–4900 (2015)

    Article  MathSciNet  Google Scholar 

  17. Cao, Y., Chen, M., Xu, B.: Theory and application of natural-based wavelet method. J. Harbin Inst. Technol. (New Ser.) 26(06), 86–90 (2019)

    MATH  Google Scholar 

  18. Liu, Y., Ji, Y., Chen, K., Qi, X.: Support Vector Regression for Bus Travel Time Prediction Using Wavelet Transform. J. Harbin Inst. Technol. (New Ser.) 26(03), 26–34 (2019)

    MATH  Google Scholar 

  19. Picariello, F., Tudosa, I., Balestrieri, E., Rapuano, S., Vito, L.D.: RF emitters localization from compressed measurements exploiting MMV-OMP algorithm. In: 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Pisa, Italy, pp. 582–587 (2020)

    Google Scholar 

  20. Zhao, C., Zhu, H., Cui, S., Qi, B.: Multiple endmember hyperspectral sparse unmixing based on improved OMP algorithm. J. Harbin Inst. Technol. 22(05), 97–104 (2015)

    Google Scholar 

  21. Kulkarni, A., Mohsenin, T.: Low overhead architectures for OMP compressive sensing reconstruction algorithm. IEEE Trans. Circuits Syst. I: Regul. Pap. 64(6), 1468–1480 (2017)

    Article  Google Scholar 

  22. Lagunas, E., Sharma, S.K., Chatzinotas, S., Ottersten, B.: Compressive sensing based target counting and localization exploiting joint sparsity. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3231–3235 (2016)

    Google Scholar 

  23. Bernal, E.A., Li, Q.: Tensorial compressive sensing of jointly sparse matrices with applications to color imaging. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2781–2785 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Juntao Sun, Guoxing Huang, Weidang Lu, Yu Zhang & Hong Peng

Authors
  1. Juntao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoxing Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weidang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guoxing Huang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Shuo Shi

  2. Harbin Institute of Technology, Weihai, China

    Ruofei Ma

  3. Zhejiang University of Technology, Hangzhou, China

    Weidang Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, J., Huang, G., Lu, W., Zhang, Y., Peng, H. (2022). Compressed Sensing Joint Image Reconstruction Based on Multiple Measurement Vectors. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04245-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04244-7

  • Online ISBN: 978-3-031-04245-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation