An adaptive order statistics filter based on fuzzy rules for image processing

An optimal filter in terms of removing noise can be determined for any signal distribution by using an order statistics (OS) filter, a typical nonlinear filter. For restoration of signals degraded by nonstationary signals such as an image signal, nonstationary noise, and mixed noise, an adaptive process in which the filter coefficients are varied in accordance with the local information is indispensable to improving signal accuracy. From these facts, an adaptive OS filter can be considered effective for image restoration. Most of the adaptive OS filters proposed so far have several fixed coefficients which are changed in accordance with the local information. However, for the restoration of signals such as deteriorated image signals, it is ideal to have the adaptive OS filter coefficients continuously change. In this paper, filter window signals are fuzzy-clustered into five predefined typical classes using a fuzzy inference system when three types of local information are the parameters of the antecedent part. Since there is an optimal OS filter for each of the five classes, we obtain the sum of outputs of the proposed filters from the filter output value which is optimal for each class, weighted by the degree of assignment of each filter class. With the proposed adaptive OS filter, the filter coefficients are varied continuously, and improvement of image restoration accuracy is attempted. (C) 1997 Scripta Technica, Inc.