Robust image wavelet shrinkage for denoising

Daniel Leo Lau, Gonzalo R. Arce, Neal C. Gallagher

Research output: Contribution to conferencePaperpeer-review

2 Citations (SciVal)


Donoho and Johnstone first introduced wavelet shrinkage as a denoising technique for signals embedded in Gaussian noise, but due to the linearity of wavelet decomposition, wavelet shrinkage is ineffective in non-Gaussian noise which exhibits outliers. In this paper, we evaluate two schemes which have been developed to extend the denoising capabilities of wavelet shrinkage to signals corrupted by non-Gaussian noise. The first scheme introduced by Bruce et al. smoother-cleaner wavelets integrates median filters into the wavelet decomposition. The second scheme, introduced by the authors, replaces the linear filters of wavelet decomposition with order statistic based Chameleon filters. We also show that a straight forward extension of these schemes to images does not offer the same effectiveness in denoising as they do with one dimensional signals.

Original languageEnglish
Number of pages4
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz
Duration: Sep 16 1996Sep 19 1996


ConferenceProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
CityLausanne, Switz

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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