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Edge detection is one of the most basic and important processes in visual signal processing. In order to carry out edge detection from an image corrupted by additive noise, it was necessary to eliminate the noise beforehand by a filter process as a preprocessing. However, since the filter process causes degradation of the original signal itself, the corrupted edge cannot be extracted if the edge detection is carried out after the filter process. The authors have proposed a method for direct edge detection by means of fuzzy inference from the image superposed only with impulsive noise. In this paper, this result is extended to propose a new edge detection method realized by fuzzy inference that carries out both the noise reduction and edge detection at the same time from images contaminated with a mixture of the impulse noise and Gaussian noise. The proposed method consists of two sets of fuzzy inference, one for estimating the number of impulsive noises and another intending to combine the Gaussian noise and edge detection. Finally, by combining these inference results, the edge signal from the mixed noise image is given. It is shown that, by varying the setting of the fuzzy sets for each inference, the degrees of edge detection and noise elimination can be varied easily in a related manner. In addition, the setting of fuzzy sets to satisfy both requirements is carried out by using a typical test image. Further, the effectiveness of the proposed method is shown through various image processing examples. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(8): 39-50, 2000 [ABSTRACT FROM AUTHOR]

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]