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Multi-focus Image Fusion Based on Fuzzy and Wavelet Transform

  • Jamal Saeedi
  • Karim Faez
  • Saeed Mozaffari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

In this paper, we proposed a new method for spatially registered multi-focus images fusion. Image fusion based on wavelet transform is the most commonly fusion method, which fuses the source images information in wavelet domain according to some fusion rules. There are some disadvantages in Discrete Wavelet Transform, such as shift variance and poor directionality. Also, because of the uncertainty about the source images contributions to the fused image, designing a good fusion rule to integrate as much information as possible into the fused image becomes one of the most important problem. In order to solve these problems, we proposed a fusion method based on double-density dual-tree discrete wavelet transform, which is approximately shift invariant and has more sub-bands per scale for finer frequency decomposition, and fuzzy inference system for fusing wavelet coefficients. This new method provides improved subjective and objectives results compared to the previous wavelet fusion methods.

Keywords

Image fusion double-density dual-tree discrete wavelet transform fuzzy classifier multi-focus 

References

  1. 1.
    Garg, S., Kiran, U., Mohan, K., Tiwary, R.: Multilevel Medical Image Fusion using Segmented Image by Level Set Evolution with Region Competition. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, January 17–18, pp. 7680–7683 (2006)Google Scholar
  2. 2.
    Yang, X.-H., Jing, Z.-L., Liu, G., Hua, L.Z.: Fusion of multi-spectral and panchromatic images using fuzzy rule. Communications in Nonlinear Science and Numerical Simulation 12, 1334–1350 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Kam, M., Zhu, X., Kalata, P.: Sensor fusion for mobile robot navigation. Proceedings of the IEEE 85, 108–119 (1997)CrossRefGoogle Scholar
  4. 4.
    Kumar, S., Senthil, M.S.: PCA-based image fusion. In: Proceedings of the SPIE, vol. 6233, p. 62331T (2006)Google Scholar
  5. 5.
    Ke, R.Z., Li, Y.-J.: An Image Fusion Algorithm Using Wavelet Transform. Acta Electronica Sinica 32(5), 750–775 (2004)Google Scholar
  6. 6.
    Kingsbury, N.: A Dual-Tree Complex Wavelet Transform with Improved Orthogonality and Symmetry Properties. In: ICIP, vol. 2, pp. 375–378 (2000)Google Scholar
  7. 7.
    Wei, S., Ke, W.: A Multi-Focus Image Fusion Algorithm with DT-CWT. In: International Conference on Computational Intelligence and Security, pp. 147–151 (2007)Google Scholar
  8. 8.
    Selesnick, I.W.: The Double-Density Dual-Tree DWT. IEEE Trans. on Signal Processing 52(5), 1304–1314 (2004)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Petrosian, Meyer, F.G.: The double density DWT. In: Wavelets in Signal and Image Analysis: From Theory to Practice, Kluwer, Boston (2001)Google Scholar
  10. 10.
    Burt, P.J., Kolczynski, R.J.: Enhanced image capture through fusion. In: Proceedings of the 4th International Conference on Computer Vision, pp. 173–182 (1993)Google Scholar
  11. 11.
    Li, H., Manjunath, B.S., Mitra, S.K.: Multi-sensor image fusion using the wavelet transform. Graphical Models and Image Processing 57(3), 235–245 (1995)CrossRefGoogle Scholar
  12. 12.
    Arivazhagan, S., Ganesan, L., Subash Kumar, T.G.: A modified statistical approach for image fusion using wavelet transform. Springer, London (2008)Google Scholar
  13. 13.
    Li, S., Kwok, J.T.: Multi-focus image fusion using artificial neural networks. Pattern Recognition Letters 23, 985–997 (2002)zbMATHCrossRefGoogle Scholar
  14. 14.
    Sendur, L., Selesnick, I.W.: Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency. IEEE Transactions on Signal Processing 50(11), 2744–2755 (2002)CrossRefGoogle Scholar
  15. 15.
    Kuncheva, L.I.: Fuzzy Classifier Design. Springer, Heidelberg (2000)zbMATHGoogle Scholar
  16. 16.
    Rockinger, O.: Image Sequence Fusion Using a Shift Invariant Wavelet Transform. In: ICIP, pp. 288–291 (1997)Google Scholar
  17. 17.
    Petrović, V., Xydeas, C.: Evaluation of image fusion performance with visible differences. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 380–391. Springer, Heidelberg (2004)Google Scholar
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jamal Saeedi
    • 1
  • Karim Faez
    • 1
  • Saeed Mozaffari
    • 2
  1. 1.Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran
  2. 2.Electrical and Computer DepartmentSemnan UniversitySemnanIran

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