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Multimedia Tools and Applications

, Volume 75, Issue 17, pp 10583–10593 | Cite as

Multifocus image fusion method of Ripplet transform based on cycle spinning

  • Peng Geng
  • Min Huang
  • Shuaiqi Liu
  • Jun Feng
  • Peina Bao
Article

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.

Keywords

Ripplet transform Cycle spinning Image fusion 

Notes

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.

References

  1. 1.
    Candès E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5(3):861–899MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Chai Y, Li H (2010) Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain. Opt Commun 283(19):3591–3602CrossRefGoogle Scholar
  3. 3.
    Das S, Chowdhury M, Kundu MK (2011) Medical image fusion based on Ripplet transform type-I. Prog Electromagn Res B 30:355–370CrossRefGoogle Scholar
  4. 4.
    Eslami R, Radha K (2003) The contourlet transform for image denoising using cycle spinning. Proceeding of Asilomar Conference on Signals, Systems, and Computers 1982–1986Google Scholar
  5. 5.
    Geng P, Wang Z, Zhang Z, Xiao Z (2012) Image fusion by pulse couple neural network with shearlet. Opt Eng 51(6):067005CrossRefGoogle Scholar
  6. 6.
    Huang W, Jing Z (2007) Evaluation of focus measures in multi-focus image fusion. Pattern Recogn Lett 28(4):493–500CrossRefGoogle Scholar
  7. 7.
    Kamilov L, Bostan E, Unser M (2012) Wavelet shrinkage with consistent cycle spinning generalizes total variation denoising. IEEE Signal Process Lett 19(4):187–190CrossRefGoogle Scholar
  8. 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):320–322Google Scholar
  9. 9.
    Liu K, Guo L, Chen J (2011) Contourlet transform for image fusion using cycle spinning. J Syst Eng Electron 22(2):353–357CrossRefGoogle Scholar
  10. 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):1168–1171Google Scholar
  11. 11.
    Miao Q, Wang B (2006) A novel image fusion method using contourlet transform. 2006 International Conference on Communications, Circuits and Systems Processing 548–552Google Scholar
  12. 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):041001-1-5Google Scholar
  13. 13.
    Qu G, Zhang D, Yan P (2002) Information measure for performance of image fusion. Electron Lett 38(7):313–315CrossRefGoogle Scholar
  14. 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):1203–1212Google Scholar
  15. 15.
    Xu J, Wu D (2008) Ripplet transform for feature extraction. The International Society for Optical Engineering 6970Google Scholar
  16. 16.
    Xu J, Yang L, Wu D (2010) Ripplet: a new transform for image processing. J Vis Commun Image Represent 21(7):627–639CrossRefGoogle Scholar
  17. 17.
    Xydeas C, Petrovic V (2000) Objective image fusion performance measure. Electron Lett 36(4):308–309CrossRefGoogle Scholar
  18. 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):1285–1295CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Peng Geng
    • 1
  • Min Huang
    • 2
  • Shuaiqi Liu
    • 3
    • 4
  • Jun Feng
    • 1
  • Peina Bao
    • 1
  1. 1.School of Information Science and TechnologyShijiazhuang Tiedao UniversityShijiazhuangChina
  2. 2.College of Biomedical EngineeringSouth-Central University for NationalitiesWuhanChina
  3. 3.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  4. 4.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina

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