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Wavelet Image Denoising Using Localized Thresholding Operators

  • M. Ghazel
  • G. H. Freeman
  • E. R. Vrscay
  • R. K. Ward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3656)

Abstract

In this paper, a localized wavelet thresholding strategy which adopts context-based thresholding operators is proposed. Traditional wavelet thresholding methods, such as VisuShrink, LevelShrink and BayesShrink, apply the conventional hard and soft thresholding operators and only differ in the selection of the threshold. The conventional soft and hard thresholding operators are point operators in the sense that only the value of the processed wavelet coefficient is taken into consideration before thresholding it. In this work, it will be shown that the performance of some of the standard wavelet thresholding methods can be improved by applying a localized, context-based, thresholding strategy instead of the conventional thresholding operators.

Keywords

Image Denoising Adaptive Wavelet Soft Thresholding Wavelet Thresholding Wavelet Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • M. Ghazel
    • 1
  • G. H. Freeman
    • 2
  • E. R. Vrscay
    • 3
  • R. K. Ward
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouver
  2. 2.Department of Electrical and Computer Engineering 
  3. 3.Department of Applied MathemaricsUniversity of WaterlooWaterloo

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