Robust Adaptive Fuzzy Filter for Impulse Noise in Grainy Images

This paper proposes an algorithm for impulsive noise in grainy images. The main objective of this algorithm is to consider a particular noisy image as input and preprocess to remove the impulsive noise content by employing suitable adaptive linear fuzzy filter after identifying the type of noise. The algorithm consists of two parts. First, identifying the type of noise present in the image as additive, multiplicative or impulsive by using image statistical parameters and secondly, denoising the impulsive noise by employing adaptive fuzzy filter. In this paper, a new noise type identification and adaptive fuzzy filtering algorithm is described. Noise present in the digital image should be removed in such a way that the important information of image should be preserved. A fuzzy Suguno inference system is used for elimination of impulsive noise in images is presented. In order to improve the performances of classical median filter, an adaptive Fuzzy filter is proposed. The proposed algorithm has been simulated on MATLAB platform. Simulation results shows that the proposed algorithm effectively identifies and removes the impulse noise by preserving image originality.