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We have presented in this paper a new enhancement technique utilizing the high-frequency component information and the edge information sensor defined in Ramponi's method. These kinds of information were used in Ramponi's method, and it is clear that they were effective for the enhancement of noisy images, carrying out enhancement of edges only without enhancing the noise. However, enough extraction is not done because of the way the two kinds of information are considered. Therefore, we proposed in this paper a method based on fuzzy inference, in which the antecedent variables are these two kinds of information and the inference result is the enhancing component. The fuzzy sets for both kinds of information were established experimentally. Through application examples we made clear that we can set the fuzzy sets independently of the kind of images or the additive noise variance, and still greatly improve the enhancement results from Ramponi's method. In this paper we focused on the tuning of the enhancement function ƒ(i,j), whose variables are the edge information sensor defined by Ramponi (square of amplitudes) and the high-frequency component information. Naturally, the scheme of computation for the edge information sensor is still one subject of study, but we leave this topic for further investigation. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(7): 1–11, 2000 [ABSTRACT FROM AUTHOR]

When edges are to be extracted from an image signal with superposed impulsive additive noises, the noise must be eliminated beforehand. In other words, two independent processing operations must be executed, in the order of noise elimination and edge detection. The elimination of the impulsive noise naturally degrades the edges of the original signal. Consequently, noise elimination and edge detection should be executed in connection with each other. This study tries to realize a new edge detector method, which includes both impulsive noise elimination and edge detection by using fuzzy inference. In the proposed method, the extent of edge detection and the extent of noise elimination can be adjusted continuously by varying the setting of the membership function for the fuzzy inference. This paper presents a detailed guide for setting the membership function through several applied examples and demonstrates the usefulness of the approach. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 83(1): 61–69, 2000 [ABSTRACT FROM AUTHOR]