A new fuzzy-based wavelet shrinkage image denoising technique

  • Authors:

  • Stefan Schulte;Bruno Huysmans;Aleksandra Pižurica;Etienne E. Kerre;Wilfried Philips

  • Affiliations:

  • Department of Applied Mathematics and Computer Science, Ghent University, Gent, Belgium;Dept. of Telecommunications and Information Processing (TELIN), IPI, Ghent University, Gent, Belgium;Dept. of Telecommunications and Information Processing (TELIN), IPI, Ghent University, Gent, Belgium;Department of Applied Mathematics and Computer Science, Ghent University, Gent, Belgium;Dept. of Telecommunications and Information Processing (TELIN), IPI, Ghent University, Gent, Belgium

  • Venue:
  • ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.