A Geometric Mean Based Adaptive Local Noise Removal Algorithm

Author

Qiaoping, Sun ; Xiaoming, Zhao

Author_Institution

Dept. of Comput., Taizhou Univ., Taizhou

fYear

2005

fDate

21-24 Nov. 2005

Firstpage

1

Lastpage

4

Abstract

Adaptive filters are important application in the signal processing field. This paper discusses the adaptive local noise removal algorithm proposed by Professor Rafael C. Gonzalez and points out its shortcomings. To improve the algorithm, a new method, geometric mean based adaptive local noise removal algorithm, has been proposed. The simulation results indicate that the new algorithm is more satisfactory. The mean square error lscr_mse is reduced by 1/4. The signals to noise ratios (i.e., SNR, SNR_m, PSRN) are raised by 1/10.This algorithm has been shown promise for applications.

Keywords

adaptive filters; mean square error methods; signal denoising; adaptive filters; adaptive local noise removal algorithm; geometric mean; mean square error; signal processing; signal to noise ratios; Adaptive filters; Additive noise; Filtering theory; Image denoising; Interference suppression; Nonlinear filters; Pixel; Signal processing algorithms; Signal to noise ratio; Wiener filter; Adaptive filtering; Geometric mean; Image de-noising; Local noise removal;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2005 2005 IEEE Region 10

Conference_Location

Melbourne, Qld.

Print_ISBN

0-7803-9311-2

Electronic_ISBN

0-7803-9312-0

Type

conf

DOI

10.1109/TENCON.2005.300955

Filename

4084919