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Video denoising by fuzzy motion and detail adaptive averaging

[+] Author Affiliations
Tom Mélange

Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281, Building S9, 9000 Gent, Belgium

Mike Nachtegael

Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281, Building S9, 9000 Gent, Belgium

Etienne E. Kerre

Ghent University, Department of Applied Mathematics and Computer Science, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281, Building S9, 9000 Gent, Belgium

Vladimir Zlokolica

MicronasNIT Institute, Fruskogorska 11, 21000 Novi Sad, Serbia and Montenegro

Stefan Schulte

Traficon N.V., Vlamingstraat 19, 8560 Wevelgem, Belgium

Valérie De Witte

Vlaamse Radio- en Televisieomroep, Auguste Reyerslaan 52, 1043 Brussel, Belgium

Aleksandra Pižurica

Ghent University, Department of Telecommunications and Information Processing IPI, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium

Wilfried Philips

Ghent University, Department of Telecommunications and Information Processing IPI, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium

J. Electron. Imaging. 17(4), 043005 (October 14, 2008). doi:10.1117/1.2992065
History: Received November 30, 2007; Revised June 20, 2008; Accepted July 31, 2008; Published October 14, 2008
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A new fuzzy-rule-based algorithm for the denoising of video sequences corrupted with additive Gaussian noise is presented. The proposed method constitutes a fuzzy-logic-based improvement of a recent detail and motion adaptive multiple class averaging filter (MCA). The method is first explained in the pixel domain for grayscale sequences, and is later extended to the wavelet domain and to color sequences. Experimental results show that the noise in digital image sequences is efficiently removed by the proposed fuzzy motion and detail adaptive video filter (FMDAF), and that the method outperforms other state of the art filters of comparable complexity on different video sequences.

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© 2008 SPIE and IS&T

Topics

Video ; Wavelets ; Denoising

Citation

Tom Mélange ; Mike Nachtegael ; Etienne E. Kerre ; Vladimir Zlokolica ; Stefan Schulte, et al.
"Video denoising by fuzzy motion and detail adaptive averaging", J. Electron. Imaging. 17(4), 043005 (October 14, 2008). ; http://dx.doi.org/10.1117/1.2992065


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