A neuro-fuzzy network based impulse noise filtering for gray scale images

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 and testing (filtering). As demonstrated by the experimental results, the proposed filter not only has the ability of noise attenuation but also possesses desirable capability of detail preservation. It significantly outperforms other conventional filters.