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)


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.


Denoising high dimensional data wavelet transforms thresholding 


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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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