Skip to main content
Springer Nature Link
Log in

Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Multi-focus image fusion technique can solve the problem that not all the targets in an image are clear in case of imaging in the same scene. In this paper, a novel multi-focus image fusion technique is presented, which is developed by using the nonsubsampled contourlet transform (NSCT) and a proposed fuzzy logic based adaptive pulse-coupled neural network (PCNN) model. In our method, sum-modified Laplacian (SML) is calculated as the motivation for PCNN neurons in NSCT domain. Since the linking strength plays an important role in PCNN, we propose an adaptively fuzzy way to determine it by computing each coefficient’s importance relative to the surrounding coefficients. Combined with human visual perception characteristics, the fuzzy membership value is employed to automatically achieve the degree of importance of each coefficient, which is utilized as the linking strength in PCNN model. Experimental results on simulated and real multi-focus images show that the proposed technique has a superior performance to series of exist fusion methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Nejati, M., Samavi, S., Shirani, S.: Multi-focus image fusion using dictionary-based sparse representation. Inf. Fusion 25, 72–84 (2014)

    Article  Google Scholar 

  2. Kumar, B.K.S.: Image fusion based on pixel significance using cross bilateral filter. Signal, Image Video Process. 9(5), 1193–1204 (2015)

    Article  Google Scholar 

  3. Liang, J., Yang, H., Ding, L., Zeng, X.: Image fusion using higher order singular value decomposition. IEEE Trans. Image Process. 21(5), 2898–2909 (2012)

    Article  MathSciNet  Google Scholar 

  4. Yang, Y., Tong, S., Huang, S., Lin, P.: Multifocus image fusion based on NSCT and focused area detection. IEEE Sens. J. 15(5), 2824–2838 (2015)

    Google Scholar 

  5. Pajares, G., Cruz, J.M.D.L.: A wavelet-based image fusion tutorial. Pattern Recognit. 37(9), 1855–1872 (2004)

    Article  Google Scholar 

  6. Yin, M., Liu, W., Zhao, X., Yin, Y., Guo, Y.: A novel image fusion algorithm based on nonsubsampled shearlet transform. Optik-Int. J. Light Electron Opt. 125(10), 2274–2282 (2014)

    Article  Google Scholar 

  7. Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process. 4(11), 1549–1560 (1995)

    Article  Google Scholar 

  8. Bhatnagar, G., Wu, Q.M.J., Liu, Z.: Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Trans. Multimed. 15(5), 1014–1024 (2013)

  9. Do, M.N., Martin, V.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)

    Article  Google Scholar 

  10. Yang, L., Guo, B.L., Ni, W.: Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1–3), 203–211 (2008)

    Article  Google Scholar 

  11. Da Cunha, A.L., Zhou, J., Do, M.N.: The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process. 15(10), 3089–3101 (2006)

    Article  Google Scholar 

  12. Kong, W., Lei, Y., Ni, X.: Fusion technique for grey-scale visible light and infrared images based on non-subsampled contourlet transform and intensity-hue-saturation transform. IET Signal Process. 5(1), 75–80 (2011)

    Article  Google Scholar 

  13. Eckhorn, R., Reitboeck, H., Arndt, M., Dicke, P.: Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput. 2(3), 293–307 (1990)

    Article  Google Scholar 

  14. Xiang, T., Yan, L., Gao, R.: A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain. Infrared Phys. Technol. 69, 53–61 (2015)

    Article  Google Scholar 

  15. Kinser, J.M.: Foveation by a pulse-coupled neural network. IEEE Trans. Neural Netw. 10(3), 621–625 (1999)

    Article  Google Scholar 

  16. Buchsbaum, G.: An analytical derivation of visual nonlinearity. IEEE Trans. Biomed. Eng. 27(5), 237–242 (1980)

    Article  Google Scholar 

  17. Qu, X.B., Yan, J.W., Xiao, H.Z., Zhu, Z.Q.: Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Autom. Sin. 34(12), 1508–1514 (2008)

    Article  MATH  Google Scholar 

  18. Huang, W., Jing, Z.: Evaluation of focus measures in multi-focus image fusion. Pattern Recognit. Lett. 28(4), 493–500 (2007)

    Article  Google Scholar 

  19. Miao, Q.G.: A novel image fusion algorithm based on local contrast and adaptive PCNN. Chin. J. Comput. 31(5), 875–880 (2008)

    Article  Google Scholar 

  20. Wang, Z.H., Zhao, B.J., Shen, T.Z.: Image fusion based on MMPN and adjustable linking strength. Acta Electron. Sin. 38(5), 1162–1166 (2010)

    Google Scholar 

  21. Sharma, K.K., Sharma, M.: Image fusion based on image decomposition using self-fractional Fourier functions. Signal, Image Video Process. 8(7), 1335–1344 (2014)

    Article  Google Scholar 

  22. Bhatnagar, G., Wu, Q.M.J., Liu, Z.: Human visual system inspired multi-modal medical image fusion framework. Expert Syst. Appl. 40(5), 1708–1720 (2013)

    Article  Google Scholar 

  23. Zhou, W., Alan Conrad, B., Hamid Rahim, S., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  24. Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electron. Lett. 38(7), 313–315 (2002)

    Article  Google Scholar 

  25. Xydeas, C.S., Petrovic, V.: Objective image fusion performance measure. Electron. Lett. 36(4), 308–309 (2000)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 61262034, 61462031, 61473221 and 61662026); Natural Science Foundation of Jiangxi Province (Nos. 20151BAB207033 and 20161ACB21015); Project of the Education Department of Jiangxi Province (Nos. KJLD14031, GJJ150461 and GJJ150438).

Author information

Authors and Affiliations

  1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Yong Yang & Yue Que

  2. School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Shu-Ying Huang

  3. Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Pan Lin

Authors
  1. Yong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Que
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shu-Ying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shu-Ying Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Que, Y., Huang, SY. et al. Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network. SIViP 11, 439–446 (2017). https://doi.org/10.1007/s11760-016-0979-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0979-1

Keywords