Using neighborhood-pixels-information and ANFIS for impulsive noise suppression

This paper introduces a novel approach for denoising the images corrupted by impulsive noise (IN) by using a new nonlinear IN suppression filter, entitled kk-nearest neighborhood pixels-based Adaptive-Fuzzy Filter (kk-AFF). The proposed filter is based on statistical impulse detection and nonlinear filtering which uses Adaptive-Network-Based Fuzzy Inference System (ANFIS) as a missed data interpolant over the kk-nearest neighbor pixels of the corrupted pixels. The impulse detection is realized by using the well-known Kolmogorov–Smirnov-based goodness-of-fit test, which yields a decision about the impulsivity of each pixel. To demonstrate the capability of kk-AFF, extensive simulations were realized revealing that the proposed filter achieves a better performance than the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, even when the images are highly corrupted by IN.