Fuzzy cluster filter

Author

Doroodchi, Mahmood ; Reza, Ali M.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA

Volume

1

fYear

1996

fDate

16-19 Sep 1996

Firstpage

939

Abstract

A generalized nonlinear filter called the fuzzy cluster filter is introduced. This filter applies fuzzy clustering inside a running-window to estimate the clean output (i.e., geometrical center of the window). This filter is capable of cancelling the heavy-tailed contaminated Gaussian noise with a good performance. The results on some signals and images demonstrate the efficacy of this approach. The performance of the method is found by calculating the mean-square-error (MSE) for different signal-to-noise ratios using Monte Carlo simulations

Keywords

Gaussian noise; Monte Carlo methods; digital filters; estimation theory; fuzzy set theory; image processing; interference suppression; nonlinear filters; Monte Carlo simulations; cancellation; clean output; fuzzy cluster filter; generalized nonlinear filter; geometrical center; heavy-tailed contaminated Gaussian noise; images; mean-square-error; performance; running-window; signal-to-noise ratios; signals; Adaptive filters; Filtering; Gaussian noise; Image segmentation; Magnetic separation; Medical simulation; Nonlinear filters; Prototypes; Signal processing; Signal to noise ratio;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.561059

Filename

561059