The learning type of mean and median hybrid filters

Author

Meguro, Mitsuhiko ; Taguchi, Akira

Author_Institution

Dept. of Electr. & Electron. Eng., Musashi Inst. of Technol., Tokyo, Japan

Volume

4

fYear

1997

fDate

21-24 Apr 1997

Firstpage

2589

Abstract

In this paper, a new adaptive filter, called a learning type of mean and median hybrid (LMMH) filters, is introduced. This filter is a combination of FIR filtering and order statistics (OS) filtering for removal all kinds of distributed noise. LMMH filter is regarded as the extension of MMH filters which can´t be learned. On the other hand, LMMH filters can be optimized by using a priori information on the input signal. A procedure for designing optimal LMMH filters under the mean square error criterion has been developed. Experimental results show that the performances of the optimal LMMH filter are superior to those of the Wiener filter and the OS filter, for signal corrupted by from short- to long-tailed distributed noise. The filters are applied to image restoration

Keywords

FIR filters; adaptive filters; image restoration; interference suppression; least mean squares methods; median filters; noise; optimisation; FIR filtering; OS filtering; adaptive filter; design; input signal; learning type of mean and median hybrid filter; long-tailed distributed noise; mean square error criterion; optimal LMMH filter; order statistics filtering; short-tailed distributed noise; Adaptive filters; Finite impulse response filter; Information filtering; Information filters; Least squares approximation; Maximum likelihood detection; Nonlinear filters; Signal processing; Statistical distributions; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.595318

Filename

595318