A Noise Density-Based Fuzzy Approach for Detecting and Removing Random Impulse Noise in Color Images

Abstract

This paper introduces a new approach aimed at restoring images corrupted by random valued impulse noise. The adopted methodology leverages fuzzy logic and encompasses three primary stages: estimation of noise density, detection of fuzzy noise, and reduction of fuzzy noise. Within the fuzzy noise detection phase, a fuzzy set labeled as "Noise-Free" is formulated through the utilization of the rank-ordered mean of absolute differences and the estimated noise density. This set serves to discern whether a given pixel should be classified as noisy or noise-free. Utilizing the fuzzy logic in the proposed method collaborates to determine the ultimate fuzzy weight assigned to each pixel, thereby facilitating the restoration of corrupted image pixels. Empirical results based on peak signal-to-noise ratio, mean square error, and visual assessment demonstrate the effectiveness of the proposed technique in suppressing noise, preserving fine details, and surpassing the performance of several established filtering methods.