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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]

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]

Nonlinear filters are known to be effective for restoration of image signals subject to additive noise. Among them, the most typical are median filters, and particularly popular are stack filters. Signal processing in the stack filters involves the following three stages: threshold value decomposition, calculation of the Boolean function output at each level, and signal synthesis. The Boolean function is defined by the binary input/output. However, to achieve better filter characteristics and design diversity, the authors considered using fuzzy Boolean functions defined by continuous output values from 0 to 1, rather than conventional Boolean functions. In the current study, the authors performed fuzzification of such common stack filters as the median filter and center weighted median (CWM) filter. Fuzzification was performed by using fuzzy Boolean functions to define the median filter or the CWM filter. The fuzzy Boolean function is derived from fuzzy inference. As a next step, the authors developed a method for fuzzy inference optimization (tuning method). © 1998 Scripta Technica, Electron Comm Jpn Pt 3, 82(2): 83–94, 1999 [ABSTRACT FROM AUTHOR]