Neo fuzzy neuron filter aiming at reduction of a Gaussian-impulsive noise

Author

Suetake, Noriaki ; Yamakawa, Takeshi

Author_Institution

Dept. of Control Eng. & Sci., Kyushu Inst. of Technol., Fukuoka, Japan

Volume

6

fYear

1999

fDate

36342

Firstpage

4324

Abstract

We propose novel FIR-OS hybrid type filter employing the neo fuzzy neuron, and frameworks of a linear FIR filter and an order statistic (OS) filter, aiming at elimination of a Gaussian noise and an impulsive noise at the same time, and high restoration of the signal, simultaneously. The proposed filter is synthesized by learning method which guarantees optimal design caused by employing the neo fuzzy neuron (NFN) model. In this paper, the effectiveness and validity of the proposed filter are verified by applying it to the filtering of the noisy images

Keywords

FIR filters; Gaussian noise; filtering theory; fuzzy neural nets; impulse noise; interference suppression; optimisation; signal processing; FIR-OS hybrid type filter; Gaussian noise elimination; Gaussian-impulsive noise; NFN model; OS filter; filter synthesis; impulsive noise elimination; linear FIR filter; neo fuzzy neuron filter; noisy image filtering; optimal design; order statistic filter; signal restoration; Finite impulse response filter; Gaussian noise; Gaussian processes; Image restoration; Learning systems; Neurons; Nonlinear filters; Signal restoration; Signal synthesis; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.830863

Filename

830863