Abstract: This paper presents the design of the technique using fuzzy inference system for contrast enhancement. It has three main stages, namely, image fuzzification, modification of member ship function values, and defuzzification. Fuzzy image enhancement is based on gray level mapping into membership function. The aim is to generate an image of higher contrast than the original image by giving a larger weight to the gray levels that are closer to the mean gray level of the image than that are farther from the mean. In the fuzzy framework of image enhancement and smoothing, two contributions merit an elaboration. The first one deals with IF..THEN..ELSE fuzzy rules for image enhancement. Here, a set of neighborhood pixels forms the antecedent part of the rule and the pixel to be enhanced is changed by the consequent part of the rule. These fuzzy rules give directives much similar to humane-like reasoning. The second one proposes a rule based filtering in which different filter classes are devised on the basis of compatibility with the neighborhood. Fuzzy Image Enhancement treats image as fuzzy set and operates on those sets.