Classifier-augmented median filters for image restoration

Developed in this paper is a new approach that augments a fuzzy classifier to determine whether or not the operating pixel, centered in the sliding window, should be involved with the impulse noise filtering process. Owing to the inclusion of the fuzzy K-nearest neighbor (K-NN) scheme, any central operating pixel that is not noise corrupted can be effectively detected and then left unchanged. Thus, the unnecessary pixel replacement can be avoided and the details and signal structure of the image will be best retained. If the center point is found to be noise corrupted, the proposed classifier-augmented median filter facilitates the filtering action only on a subset of pixels, which are not noise contaminated in the window. Due to this impulse pixel exclusion, the biased estimation caused from impulses can be eliminated and, thus, obtains a better estimation of the center pixel. Experimental results showed that this new approach largely outperformed several existing schemes for image noise removal.