Research On Image Enhancement Algorithms Based On Fuzzy Set Theory

As a means of low level image processing, image enhancement usually has ambiguity and vagueness. The development of fuzzy theory and the status quo of image enhancement based on fuzzy theory are briefly introduced. And the image enhancement algorithms in existence based on fuzzy set theory, esp. the fuzzy membership functions and the INT's, are analyzed and compared. Firstly, a novel fuzzy entropy definition is proposed based on the analyzing the existed fuzzy entropy definitions and the exponential behavior of information-gain. Self-adaptive image enhancement is achieved with the novel fuzzy entropy definition and the maximum entropy principle using the enumerating algorithm. And the experiment results show that the effect of the self-adaptive image enhancement is good both in subject evaluation and object evaluation and that its processing time is shorter than that of other algorithms based on the existed fuzzy entropy definitions. Secondly, the self-adaptive enhancement algorithm based on enumerating algorithm is improved with the help of the searching optimum capacity in wholeness and the parallelism of genetic algorithm in order to save processing time and close with its real time applications. And the new fuzzy entropy definition is taken as fitness function. Its experiment results show that the subject evaluation and object evaluation are not changed and that the processing time of the algorithm's optimum part is shorter 60% than that of enumerating algorithm. At last, a fuzzy enhancement operator which can detect images' edges faster and better than GFOs is proposed based on the general fuzzy enhancement and the properties of the object region, background region and edges region of images. And the experiment results show that the algorithm is especially fit for low contrast images and images including fine parts and much texture and that processing time is cut down a little.