Skip to main content
SpringerLink
Log in

Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation

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

Abstract

The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Agrafioti F, Gao JX, Mohammadzade H (2011) A 2D Bivariate EMD algorithm for image fusion, 17th International Conference on Digital Signal Processing (DSP). IEEE. p. 1–6

  2. Aishwarya N, Bennila Thangammal C (2017) An image fusion framework using morphology and sparse representation. Multimed Tools Appl 77(8):9719–9736

  3. An FP, Liu ZW (2019) Bi-dimensional empirical mode decomposition (BEMD) algorithm based on particle swarm optimization-fractal interpolation. Multimed Tools Appl 78(12):17239–17264

    Article  Google Scholar 

  4. Bhuiyan SMA, Adhami RR, Khan JF (2008) Fast and Adaptive Bidimensional Empirical Mode Decomposition Using Order-Statistics Filter Based Envelope Estimation. EURASIP J Adv Signal Process 28(1):1–18

    Google Scholar 

  5. Chen S, Donoho D, Saunders M (2001) Atomic decomposition by basis pursuit. SIAM Rev 43(1):129–159

    Article  MathSciNet  Google Scholar 

  6. Chen SH, Su HB, Zhang RH et al (2008) Fusing remote sensing images using à trous wavelet transform and empirical mode decomposition. Pattern Recogn Lett 29:330–342

    Article  Google Scholar 

  7. Chen Q, Zhang G, Yang X et al (2018) Single image shadow detection and removal based on feature fusion and multiple dictionary learning. Multimed Tools Appl 77(14):18601–18624

  8. Deyan W, Yin X, Ya G (2019) Image Denoising method based on NSCT bivariate model and Variational Bayes threshold estimation. Multimed Tools Appl 78(7):8927–8941

    Article  Google Scholar 

  9. Engan K, Aase SO, Husoy JK (2000) Multi-frame compression: theory and design. EURASIP Signal Process 80(10):2121–2140

    Article  Google Scholar 

  10. Gorodnitsky I, Rao B (1997) Sparse signal reconstruction from limited data using FOCUSS: a re-weighted norm minimization algorithm. IEEE Trans Signal Process 45(3):600–616

    Article  Google Scholar 

  11. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc London A 454:903–995

    Article  MathSciNet  Google Scholar 

  12. Huang NE, Shen Z, Long SR (1999) A new view of nonlinear water waves: the Hilbert spectrum. Annu Rev Fluid Mech 31:417–457

    Article  MathSciNet  Google Scholar 

  13. Huang Y, Zhou F, Gilles J (2019) Empirical curvelet based fully convolutional network for supervised texture image segmentation. Neurocomputing 349(JUL.15):31–43

    Article  Google Scholar 

  14. Jamal S, Karim F (2012) Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl Soft Comput 12:1041–1054

    Article  Google Scholar 

  15. Jong WD, Cremer F, Schutte K et al (2000) Usage of polarization features of landmines for improved automatic detection. Proc SPIE 4038:1–12

    Google Scholar 

  16. Kong WW, Yang L (2013) Technique for image fusion between gray-scale visual light and infrared images based on NSST and improved RF. Optik 124:6423–6431

    Article  Google Scholar 

  17. Li ST, Yang B, Hu JW (2011) Performance comparison of different multi-resolution transforms for image fusion. Info Fusion 12:74–84

    Article  Google Scholar 

  18. Liu ZD, Yin HP, Fang B et al (2015) A novel fusion scheme for visible and infrared images based on compressive sensing. Opt Commun 335:168–177

    Article  Google Scholar 

  19. Liu Y, Liu SP, Wang ZF (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion 24:147–164

    Article  Google Scholar 

  20. Looney D, Mandic DP (2009) Multiscale Image Fusion Using Complex Extensions of EMD. IEEE Trans Signal Process 57(4):1626–1630

    Article  MathSciNet  Google Scholar 

  21. Ma C, Zhu J, Li Y et al (2020) Single image super resolution via wavelet transform fusion and SRFeat network. J Ambient Intell Humaniz Comput:1–9

  22. Mahbubur Rahman SM, Omair Ahmad M, Swamy MNS (2010) Contrast-based fusion of noisy images using discrete wavelet transform. IET Image Process 4(5):374–384

    Article  MathSciNet  Google Scholar 

  23. Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  25. Michal A, Elad M, Bruckstein A (2006) K-SVD: An algorithm for designing over complete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    Article  Google Scholar 

  26. Pati YC, Rezaiifar R, Rezaiifar YCPR et al. (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, In: Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers. 1:40–44

  27. Rehman N, Mandic DP (2010) Multivariate empirical mode decomposition. Proc R Soc A 466:1291–1302

    Article  MathSciNet  Google Scholar 

  28. Rilling G, Flandrin P, Gonçalves P (2007) Bivariate Empirical Mode Decomposition. IEEE Signal Process Lett 14(12):936–939

    Article  Google Scholar 

  29. Scott Tyo J, Goldstein DL, Chenault DB et al (2006) Shaw, Review of passive imaging polarimetry for remote sensing applications. Appl Opt 45(22):5453–5469

    Article  Google Scholar 

  30. Scott Tyo J, Goldstein DH, Chenault DB (2006) Polarization in Remote Sensing-introduction. Appl Opt 45(22):5451–5452

    Article  Google Scholar 

  31. Shi DF, Hu SX, Wang YJ (2014) Polarimetric ghost imaging. Opt Lett 39(5):1231–1234

    Article  Google Scholar 

  32. Wang Z, Cui Z, Zhu Y (2020) Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation. Comp Biol Med 123:103823

  33. Xingmin M, Xianwei Z, Fengping A (2019) Fast Bi-dimensional empirical mode decomposition(BEMD) based on variable neighborhood window method. Multimed Tools Appl 78:8889–8910

    Article  Google Scholar 

  34. Yang B, Li ST (2010) Multifocus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas 59(4):884–892

    Article  Google Scholar 

  35. Yang B, Li ST (2012) Pixel-level image fusion with simultaneous orthogonal matching pursuit. Info Fusion 13:10–19

    Article  Google Scholar 

  36. Yang FB, Wei H (2013) Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules. Infrared Phys Technol 60:235–243

    Article  Google Scholar 

  37. Yu NN, Qiu TS, Bi F et al (2011) Image Features Extraction and Fusion Based on Joint Sparse Representation. IEEE J Select Topics Signal Process 5(5):1074–1082

    Article  Google Scholar 

  38. Yue Z, Li FM (2014) An infrared polarization image fusion algorithm based on oriented Laplacian pyramid, Proc. of SPIE. 914208-1-914208-11

  39. Zhu P, Huang ZH, Lei H (2016) Fusion of infrared and visible images based on BEMD and NSDFB. Infrared Phys Technol 77:82–93

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the editors and the reviewers for their careful work and invaluable suggestions for helping us to improve this paper. We express our sincere thanks to Yu Liu [19] for sharing MST-SR toolbox. This work is supported by the National Natural Science Foundation of China (Grant No: 61901310, E080703, 51778509).

Author information

Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education|, Wuhan University of Science and Technology, Wuhan, 430081, China

    Pan Zhu, Lu Liu & Xinglin Zhou

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Pan Zhu, Lu Liu & Xinglin Zhou

Authors
  1. Pan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinglin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pan Zhu.

Ethics declarations

Conflict of interest

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

Zhu, P., Liu, L. & Zhou, X. Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation. Multimed Tools Appl 80, 4455–4471 (2021). https://doi.org/10.1007/s11042-020-09860-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09860-z

Keywords

Search

Navigation