A Two-Stage Fuzzy Filtering Method to Restore Images Contaminated by Mixed Impulse and Gaussian Noises

Abstract

In this paper, we propose a two-stage fuzzy filtering method to sequentially remove the mixed noises of images corrupted with nonlinear impulse and linear Gaussian noises as well. In the first stage, a new decision-based method, called nonlinear fuzzy K-nearest neighbor (FK-NN) filter, detect and replace the outlier pixels, based on local processing window, to remove the nonlinear impulse noise. Then we derive a linear modified fuzzy rule-based (MFRB) filter to remove the linear type Gaussian noise while preserving the image edges and details as much as possible. For practical consideration, we design several sets of universal MFRB filters in correspondence to the estimated values of contaminated Gaussian noise variance in the image. The correspondent MFRB filter closest to the estimated Gaussian noise level will be selected to remove the Gaussian noise of the processed image. According to the experiment results, the proposed method is superior, both quantitatively and visually, compared to several other techniques.