A simple neuro-fuzzy method for improving the performances of impulse noise filters for digital images

Abstract A novel method for improving the performances of impulse noise filters is presented. The method enhances the performance of an impulse noise filter in two ways: increases its noise-suppression ability and decreases its distortion effects. The method is based on a simple 2-input 1-output neuro-fuzzy system. The internal parameters of the system are tuned by training. Training of the system is easily accomplished by using a simple computer-generated artificial image. The proposed method can easily be used with any impulse noise removal operator. The application of the method is completely independent of the noise removal operator and it has no influence on the filtering behavior of the operator. Experimental results show that the proposed method may efficiently be used with any type of impulse noise removal operator to significantly improve its filtering performance.

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