Do fuzzy techniques offer an added value for noise reduction in images?

  • Authors:

  • M. Nachtegael;S. Schulte;D. Van der Weken;V. De Witte;E. E. Kerre

  • Affiliations:

  • Dept. of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Ghent University, Gent, Belgium;Dept. of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Ghent University, Gent, Belgium;Dept. of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Ghent University, Gent, Belgium;Dept. of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Ghent University, Gent, Belgium;Dept. of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Ghent University, Gent, Belgium

  • Venue:
  • ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2005

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Abstract

In this paper we discuss an extensive comparative study of 38 different classical and fuzzy filters for noise reduction, both for impulse noise and gaussian noise. The goal of this study is twofold: (1) we want to select the filters that have a very good performance for a specific noise type of a specific strength; (2) we want to find out whether fuzzy filters offer an added value, i.e. whether fuzzy filters outperform classical filters. The first aspect is relevant since large comparative studies did not appear in the literature so far; the second aspect is relevant in the context of the use of fuzzy techniques in image processing in general.