1. Design of data-dependent α-trimmed mean filters using counterpropagation networks.
- Author
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Ochiai, Takashi, Muneyasu, Mitsuji, Sasaki, Kazuya, and Hinamoto, Takao
- Subjects
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FILTERS (Mathematics) , *RANDOM noise theory , *IMAGE processing , *STOCHASTIC information theory , *UNCERTAINTY (Information theory) , *STOCHASTIC processes , *INFORMATION processing , *GAUSSIAN processes - Abstract
Elimination of mixed noise consisting of white Gaussian noise and impulsive noise is one of the important problems in image processing. In this paper, a data-dependent α-trimmed mean filter using backpropagation networks is proposed. In order to determine an appropriate value of α for each processing point, the pattern classification capability of the backpropagation networks is used. Further, by processing taking into account the edge portions, both edge preservation and mixed noise elimination are attained simultaneously. Finally, the effectiveness of the proposed method is verified by simulation. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(7): 30–40, 2003; Published online in Wiley InterScience (
www.interscience.wiley.com ). DOI 10.1002/ecjc.10061 [ABSTRACT FROM AUTHOR]- Published
- 2003
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