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A neuro-fuzzy network based impulse noise filtering for gray scale images is presented. The proposed filter is constructed by combining two neuro-fuzzy filters with a postprocessor, which generates the final output. Each neuro-fuzzy filter is a first order Sugeno type fuzzy inference system with 4-inputs and 1-output. The proposed impulse noise filter consists of two modes of operation, namely, training...
Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, an effective impulse noise filter for color images is presented. It consists of two modes of operation, namely, training and testing (filtering). During training, the impulse detector is used to locate the noisy pixels in the color images for optimizing the RNF network. During testing, if a pixel is detected...
Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, a new effective filter for color images corrupted by impulse noise is presented in this paper. There are two distinctive properties of the proposed method. The first is the use of a simple impulse detector. The other is the use of a novel membership function in the design of the adaptive RNF network, making...
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