A comparison of image filtering algorithms

Abstract

This paper compares four image filtering algorithms on common data sets for various signal to noise ratios and white Gaussian noise. The algorithms are the median filter, the Wallis filter, the reduced update Kalman filter, and a multiple model, decision-directed filter. This comparison produces some surprising results from both a subjective (visual error) and a mean square error (MSE) viewpoint.