Fuzzy Rule-Based Image Processing with Optimization
Fuzzy rule-based image processing technologies for noise reduction and edge extraction are described. Here, two types of noises are considered in noise reduction, namely white Gaussian noise and impulsive noise. Fuzzy rules are applied in order to consider the nonstationarity and uncertainty of signals. Moreover, the fuzzy reasoning part is designed optimally by expressing the system as a nonlinear function of multiple local characteristics of signals, and by setting the nonlinear function so that the mean square error of the output is the minimum for some training image data. Accordingly, the membership function and the rules are automatically designed from this optimization. Computer simulations verify the effective performance of this image processing technology.
KeywordsMembership Function Fuzzy Rule Impulse Noise Edge Image Impulsive Noise
Unable to display preview. Download preview PDF.
- 3.Arakawa K. and Arakawa Y., A nonlinear digital filter using fuzzy clustering, in: “Proceedings of IEEE-ICASSP’92”, Vol. IV, pp. 309–312, 1992Google Scholar
- 4.Arakawa K., Fuzzy rule-based signal processing and its application to image restoration, IEEE, Journal on Select. Areas in Comm., Vol. 12, No. 9, 1994Google Scholar
- 6.Arakawa K., Fuzzy rule-based edge detection using multiscale edge images,in: “Proceedings of IEEE-ISPACS’98”, pp. 204–208, 1998Google Scholar
- 7.Harashima H., Odajima K., Shishikui Y and Miyakawa H., e-separating nonlinear digital filter and its application, Trans. IEICE Japan, Vol. J65-A, No. 4, pp. 297–304, 1982Google Scholar
- 8.Haykin S., “Introduction to adaptive filters”, Macmillan, 1984Google Scholar
- 13.Robinson G.S., Edge detection by compass gradient masks, CGIP, Vol. 6, pp. 492–501, 1977Google Scholar