Improved Evolutionary Hybrid Method for Designing Morphological Operators

This paper presents an improved evolutionary hybrid method for designing morphological operators via the Matheron and the Banon and Barrera decompositions of translation invariant operators. It consists of a hybrid model composed of a modular morphological neural network (MMNN) and an improved genetic algorithm (IGA) having optimal genetic operators to accelerate convergence of the genetic algorithm. The proposed design method looks for initial weights, architecture and number of modules in the MMNN; then each element of the IGA population is trained via the back propagation (BP) algorithm. Optimal morphological operators are applied to image restoration and edge extraction of binary images corrupted by salt and pepper noise. The method proposed herein is capable of performing simultaneous edge extraction and noise removal operations, allowing seamless and efficient design of morphological operators of either increasing or non-increasing types.