Adaptive filtering using morphological operators and genetic algorithms

Morphological filters are an important class of nonlinear image processing and analysis tools. In the recent years they have found a wide range of applications such as noise reduction, edge detection and object recognition, adaptive filters being also reported. It is accepted that the design of a filter for a specific task requires a good knowledge of mathematical morphology. In this paper we investigate a genetic algorithm approach for finding an optimal morphological filter, using a combination of classical operators and adaptive ones. Some examples in applying the proposed method in noise reduction tasks are shown.