An image noise reduction technique based on the fuzzy rules

Aiming at that an image is typical non-stationary signal, an image noise reduction technique based on the fuzzy rules is proposed. This image processing system (IPS )is realized as a time-variant system in which the system parameters change continuously depending on the local characteristics of the images. Here Gaussian noise is considered in noise reduction. The fuzzy rules are used to consider the unstableness and uncertainty of signals. The nonlinear function representing the fuzzy rule-based IPS depends on the rules concerning the local characteristics of the input, on the membership functions, and on the used defuzzification method. In order to make the system performance as high as possible, these factors must be determined to be the most appropriate ones. In this paper a method for designing the optimum nonlinear function directly from the local characteristics of training data is presented. Here the rules, the membership functions, and the way of defuzzification are not necessary to be known. The design of these factors are involved in the design of the membership function, thus obtaining the optimum nonlinear function is enough for designing the IPS. The only thing needed to do is to decide what sort of the local characteristics of the image should be applied to the rule-based system. Computer simulations show that the proposed technique gives better results in comparison with that of the weighted averaging filter and median filter. The technique performance has been verified.