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Denoising of Three Dimensional Data Cube Using Bivariate Wavelet Shrinking

  • Guangyi Chen
  • Tien D. Bui
  • Adam Krzyzak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6111)

Abstract

The denoising of a natural signal/image corrupted by Gaussian white noise is a classical problem in signal/image processing. However, it is still in its infancy to denoise high dimensional data. In this paper, we extended Sendur and Selesnick’s bivariate wavelet thresholding from two-dimensional image denoising to three dimensional data denoising. Our study shows that bivariate wavelet thresholding is still valid for three dimensional data. Experimental results confirm its superiority.

Keywords

Denoising high dimensional data wavelet transforms thresholding 

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References

  1. 1.
    Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Chen, G.Y., Bui, T.D.: Multiwavelet denoising using neighbouring Coefficients. IEEE Signal Processing Letters 10(7), 211–214 (2003)CrossRefGoogle Scholar
  3. 3.
    Bui, T.D., Chen, G.Y.: Translation-invariant denoising using multiwavelets. IEEE Transactions on Signal Processing 46(12), 3414–3420 (1998)CrossRefGoogle Scholar
  4. 4.
    Chen, G.Y., Bui, T.D., Krzyzak, A.: Image denoising using neighbouring wavelet coefficients. Integrated Computer-Aided Engineering 12(1), 99–107 (2005)Google Scholar
  5. 5.
    Chen, G.Y., Bui, T.D., Krzyzak, A.: Image Denoising with Neighbour Dependency and Customized Wavelet and Threshold. Pattern Recognition 38(1), 115–124 (2005)CrossRefGoogle Scholar
  6. 6.
    Chen, G.Y., Kegl, B.: Image denoising with complex ridgelets. Pattern Recognition 40(2), 578–585 (2007)zbMATHCrossRefGoogle Scholar
  7. 7.
    Chen, G.Y., Qian, S.E.: Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis. Canadian Journal of Remote Sensing 34(5), 447–454 (2008)Google Scholar
  8. 8.
    Chen, G.Y., Qian, S.E.: Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis. International Journal of Remote Sensing (to appear)Google Scholar
  9. 9.
    Sendur, L., Selesnick, I.W.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing 50(11), 2744–2756 (2002)CrossRefGoogle Scholar
  10. 10.
    Sendur, L., Selesnick, I.W.: Bivariate shrinkage with local variance estimation. IEEE Signal Processing Letters 9(12), 438–441 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Guangyi Chen
    • 1
  • Tien D. Bui
    • 2
  • Adam Krzyzak
    • 2
  1. 1.Department of Mathematics and StatisticsConcordia UniversityMontrealCanada
  2. 2.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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