A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

  • Stefan Schulte
  • Bruno Huysmans
  • Aleksandra Pižurica
  • Etienne E. Kerre
  • Wilfried Philips
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


This paper focuses on fuzzy image denoising techniques. In particular, we investigate the usage of fuzzy set theory in the domain of image enhancement using wavelet thresholding. We propose a simple but efficient new fuzzy wavelet shrinkage method, which can be seen as a fuzzy variant of a recently published probabilistic shrinkage method [1] for reducing adaptive Gaussian noise from digital greyscale images. Experimental results show that the proposed method can efficiently and rapidly remove additive Gaussian noise from digital greyscale images. Numerical and visual observations show that the performance of the proposed method outperforms current fuzzy non-wavelet methods and is comparable with some recent but more complex wavelets methods. We also illustrate the main differences between this version and the probabilistic version and show the main improvements in comparison to it.


Fuzzy Rule Additive Gaussian Noise Wavelet Shrinkage Shrinkage Method Gaussian Scale Mixture 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Schulte
    • 1
  • Bruno Huysmans
    • 2
  • Aleksandra Pižurica
    • 2
  • Etienne E. Kerre
    • 1
  • Wilfried Philips
    • 2
  1. 1.Department of Applied Mathematics and Computer ScienceGhent UniversityGentBelgium
  2. 2.Dept. of Telecommunications and Information Processing (TELIN), IPIGhent UniversityGentBelgium

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