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The authors propose a new method for restoring images corrupted by high-probability impulsive noise at 10 to 50%. The strength (number of instances) of impulsive noise present in the area adjacent to a processing point is not uniform even in images that are corrupted by high-probability impulsive noise. In addition, when a processing point itself does not represent high-probability impulsive noise, outputting the input signal as is without performing median filter processing is vital to avoid image degradation. Given the above, one approach for restoring images degraded by high-probability impulsive noise is to first determine whether or not a processing point represents impulsive noise, and then perform median filter processing only when it is determined that impulsive noise is present. Second, when performing median filter processing, the amount of impulsive noise adjacent to a processing point must be estimated, the size of the filter window appropriate to that amount must be set, and efforts to preserve the signal must be made. In the proposed method the authors were able to unify three types of fuzzy inference. In addition, the proposed method is superior at handling images degraded by impulsive noise over a probability range of 10 to 50% when the fuzzy sets are fixed by setting the fuzzy sets in the three fuzzy inferences appropriately. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(9): 43–57, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1118 [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]

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]

A filter using fuzzy inference has been proposed as a noise reduction filter for images with superposed mixed noise and its effectiveness has been demonstrated. When fuzzy inference is used, the optimization of its parameters is often a problem. For instance, Takashima and colleagues have proposed a configuration consisting of a cascaded connection of fuzzy controllers for impulsive noise and Gaussian noise. It is difficult to carry out optimization of both fuzzy controllers at the same time. In this paper, an optimization method is proposed for fuzzy controllers cascaded or connected in a layered manner. This method is applied to the fuzzy controller in the edge-preserving nonlinear filter proposed by Takashima. In this way the structure is extended to a tunable filter. From the simulation results, it is found that the proposed method is effective for performance enhancement of the filter and is also effective for adjustment of fuzzy controllers connected in a layered manner. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(5): 76–88, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1150 [ABSTRACT FROM AUTHOR]

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]