An edge detection from images corrupted by mixed noise using fuzzy inference

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 caries out both the noise reduction and edge defection 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. (C) 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(8):39-50, 2000.