Title :
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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830863
