Optimization design of soft morphological filters based on improving genetic algorithm

Soft morphological filters form a large subclass of stack filters. They were introduced to improve the behavior of standard morphological filters in noisy conditions. But design methods existing for these filters tend to be computationally intractable or require some special knowledge. Genetic algorithms provide useful tools for optimization problems. In this paper, a method of soft morphological filter design using improving genetic algorithm is described, and the behavior of soft morphological filters is illustrated by empirical results. Some optimal parameters are also proposed for MAE and MSE error criteria.