Exploration of intelligent techniques for image filtering

Author

Suthaharan, Shun ; Zhang, Zhongwei

Author_Institution

Sch. of Comput. & Inf. Technol., Monash Univ., Churchill, Australia

fYear

1998

fDate

2-4 Mar 1998

Firstpage

211

Lastpage

216

Abstract

The soft computing techniques have been introduced in image processing in recent years and a number of promising results have been achieved. In this paper, two new techniques based on soft computing have been proposed to restore images that are degraded by blur and noise. The first technique is based on hierarchical fuzzy logic and the second is based on genetic algorithms. The hierarchical fuzzy approach is used to perform detail sharpening of an image corrupted by noise, whilst the genetic algorithm approach is used to find the optimal value of the noise-to-signal power ratio in the Wiener filter technique to restore blurred-noisy images. However, the second method can be used in a control experiment in conjunction with any image-quality-assessment measure. The measure used in this paper is the minimum mean square error (MSE) to demonstrate the effectiveness of the method

Keywords

filtering theory; fuzzy logic; genetic algorithms; image enhancement; image restoration; interference suppression; MSE; Wiener filter technique; detail sharpening; genetic algorithms; hierarchical fuzzy logic; image blur; image filtering; image noise; image quality assessment measure; image restoration; intelligent techniques; minimum mean square error; noise-to-signal power ratio; optimal value; soft computing techniques; Additive noise; Degradation; Equations; Fuzzy logic; Genetic algorithms; Image restoration; Layout; Pixel; Signal to noise ratio; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Devices, Circuits and Systems, 1998. Proceedings of the 1998 Second IEEE International Caracas Conference on

Conference_Location

Isla de Margarita

Print_ISBN

0-7803-4434-0

Type

conf

DOI

10.1109/ICCDCS.1998.705835

Filename

705835