Fuzzy selection filters for image restoration with neural learning

Author

Chen, Rong-Chung ; Yu, Pao-Ta

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Chung Cheng Univ., China

Volume

1

fYear

1997

fDate

9-12 Jun 1997

Firstpage

593

Abstract

In this paper, a novel class of nonlinear filters, called rank conditioned fuzzy selection (RCFS) filters, is proposed to improve the filtering performance and adaptive capability of rank conditioned rank selection (RCRS) filters. The output of this new class of nonlinear filters is determined by the center of gravity of a selected fuzzy rank which is a fuzzy set of the ranks of the observation samples. Since the membership functions in each fuzzy rank of the RCFS filters are trained by neural learning, they are more adaptive for noise filtering. The computer simulation shows that the RCFS filters have better filtering capability than the RCRS filters and other conventional filters

Keywords

adaptive filters; filtering theory; fuzzy neural nets; fuzzy set theory; image restoration; learning (artificial intelligence); nonlinear filters; adaptive capability; filtering performance; image restoration; membership functions; neural learning; nonlinear filters; observation samples; rank conditioned fuzzy selection filters; weighting functions; Adaptive filters; Computer science; Computer simulation; Fuzzy sets; Gravity; Image restoration; Information filtering; Information filters; Nonlinear filters; Zinc;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on

Print_ISBN

0-7803-3583-X

Type

conf

DOI

10.1109/ISCAS.1997.608829

Filename

608829