A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images

A new impulse noise detector based on neuro-fuzzy methods is presented. The proposed detector comprises two identical neuro-fuzzy subdetectors combined with a decision maker. The internal parameters of the subdetectors are adaptively adjusted by training. Training of the subdetectors is accomplished by using a simple computer generated artificial image. The detector can be combined with any impulse noise removal operator. The operation of the detector 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 detector significantly reduces the distortion effects of any impulse noise removal operator even if the operator already has its own noise detector.