Contrast Enhancement of Gray Scale Images Based on the Random Walk Model

Abstract

In this paper a new approach to the problem of contrast enhancement of gray scale images is presented. The algorithms introduced here are based on a model of a virtual particle, which performs a random walk on the image lattice. It is assumed, that the probability of a transition of the walking particle from a lattice point to a point belonging to its neighbourhood is determined by the Gibbs distribution, defined on a specified neighbourhood system.

In this work four algorithms of contrast enhancement are presented. The first algorithm traces the visits of the walking particle and determines their relative frequencies. The second operator assigns to each lattice point the probability of a stationary Markov chain, generated by the trajectory of the randomly walking particle. The third algorithm is based on a concept of a jumping particle and the last one uses the information contained in the statistical sum of the Gibbs distribution of the transition probabilities.