Adaptive median filter based on ANFIS for impulse noise suppression
Anissa Selmani, Hassene Seddik, Ben braiek Ezzedine · 2014
Image enhancement and restoration in a noisy environment are fundamental problems in image processing. Various filtering techniques have been developed to suppress noise in order to improve the quality of images. Among diverse de-noising techniques, median filter is a well-known filter to deal with impulse noise in digitals images. However, due to some limitations associated with the standard median filtering approach, several new improved versions of the median filtering method have been proposed by researchers. In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for restoring digital images corrupted by salt and pepper noise by a dynamic median filter that will adapt itself to the local noise intensity. Simulation results indicate that the proposed approach shows a high-quality restoration of filtered images than those using static median filter or others filters, in terms of peak signal-to-noise ratio (PSNR).