Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Multifocus image fusion method of Ripplet transform based on cycle spinning

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

Abstract

The curvelet transform can represent images at both different scales and different directions. Ripplet transform, as a higher dimensional generalization of the curvelet transform, provides a new tight frame with sparse representation for images with discontinuities along C2 curves. However, the ripplet transform is lack of translation invariance, which causes the pseudo-Gibbs phenomenon on the edges of image. In this paper, the cycle spinning method is adopted to suppress the pseudo-Gibbs phenomena in the multifocus image fusion. On the other hand, a modified sum-modified-laplacian rule based on the threshold is proposed to make the decision map to select the ripplet coefficient. Several experiments are executed to compare the presented approach with other methods based on the curvelet, sharp frequency localized contourlet transform and shearlet transform. The experiments demonstrate that the presented fusion algorithm outperforms these image fusion works.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Candès E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5(3):861–899

    Article  MathSciNet  MATH  Google Scholar 

  2. Chai Y, Li H (2010) Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain. Opt Commun 283(19):3591–3602

    Article  Google Scholar 

  3. Das S, Chowdhury M, Kundu MK (2011) Medical image fusion based on Ripplet transform type-I. Prog Electromagn Res B 30:355–370

    Article  Google Scholar 

  4. Eslami R, Radha K (2003) The contourlet transform for image denoising using cycle spinning. Proceeding of Asilomar Conference on Signals, Systems, and Computers 1982–1986

  5. Geng P, Wang Z, Zhang Z, Xiao Z (2012) Image fusion by pulse couple neural network with shearlet. Opt Eng 51(6):067005

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Kamilov L, Bostan E, Unser M (2012) Wavelet shrinkage with consistent cycle spinning generalizes total variation denoising. IEEE Signal Process Lett 19(4):187–190

    Article  Google Scholar 

  8. Liang D, Li Y, Shen M et al (2007) An algorithm for multi-focus image fusion using wavelet based contourlet transform [J]. Acta Electron Sin 35(2):320–322

    Google Scholar 

  9. Liu K, Guo L, Chen J (2011) Contourlet transform for image fusion using cycle spinning. J Syst Eng Electron 22(2):353–357

    Article  Google Scholar 

  10. Ma D, Xue Q, Chai Q, Ren B (2011) Infrared and visible images fusion method based on image information. Infrared Laser Eng 40(6):1168–1171

    Google Scholar 

  11. Miao Q, Wang B (2006) A novel image fusion method using contourlet transform. 2006 International Conference on Communications, Circuits and Systems Processing 548–552

  12. Miao Q, Shi C, Xu P, Yang M, Shi Y (2011) Multi-focus image fusion algorithm based on shearlets. Chin Opt Lett 9(4):041001-1-5

    Google Scholar 

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

    Article  Google Scholar 

  14. Qu X, Yan J, Yang G (2009) Sum-modified-laplacian-based multifocus image fusion method in sharp frequency localized contourlet transform domain. Opt Precis Eng 17(5):1203–1212

    Google Scholar 

  15. Xu J, Wu D (2008) Ripplet transform for feature extraction. The International Society for Optical Engineering 6970

  16. Xu J, Yang L, Wu D (2010) Ripplet: a new transform for image processing. J Vis Commun Image Represent 21(7):627–639

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Zhao H, Li Q, Feng H (2008) Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map. Image Vis Comput 26(9):1285–1295

    Article  Google Scholar 

Download references

Acknowledgments

Some of the images used in this paper are available at http://www.imagefusion.org. This work was supported in part by Natural Science Foundation of China under grant 30970782, Natural Science Foundation of Hebei Province under grant 2013210094 and F2013210109, Science and Technology Research and Development of Hebei Province under grant 10213516D, the University Science Research Project of Hebei Education Department under grant 201142. The authors also thank the editors and anonymous reviewers for their valuable suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peng Geng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Geng, P., Huang, M., Liu, S. et al. Multifocus image fusion method of Ripplet transform based on cycle spinning. Multimed Tools Appl 75, 10583–10593 (2016). https://doi.org/10.1007/s11042-014-1942-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1942-1

Keywords

Navigation