A new fuzzy motion and detail adaptive video filter

  • Authors:

  • Tom Mélange;Vladimir Zlokolica;Stefan Schulte;Valérie De Witte;Mike Nachtegael;Aleksandra Pizurica;Etienne E. Kerre;Wilfried Philips

  • Affiliations:

  • Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Gent, Belgium;MicronasNIT Institute, Novi Sad, Serbia&Montenegro;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Gent, Belgium;Ghent University, Dept. of Telecommunications and Information Processing, TELIN, IPI, Gent, Belgium;Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Gent, Belgium;Ghent University, Dept. of Telecommunications and Information Processing, TELIN, IPI, Gent, Belgium

  • Venue:
  • ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper a new low-complexity algorithm for the denoising of video sequences is presented. The proposed fuzzy-rule based algorithm is first explained in the pixel domain and later extended to the wavelet domain. The method can be seen as a fuzzy variant of a recent multiple class video denoising method that automatically adapts to detail and motion. Experimental results show that the proposed algorithm efficiently removes Gaussian noise from digital greyscale image sequences. These results also show that our method outperforms other state-of-the-art filters of comparable complexity for different video sequences.