Modular Morphological Neural Network Training via Adaptive Genetic Algorithm for Designing Translation Invariant Operators

In the present paper, adaptive genetic algorithm (AGA) is used for training a modular morphological neural network (MMNN) for designing translation invariant operators via Matheron decomposition and via Banon and Barrera decomposition. The operators are applied to restoration of images corrupted by salt and pepper noise. The AGA is used to determine the weights, architecture and number of modules of the MMNN. Results in terms of noise to signal ratio show that the method proposed in the present work lead to a better operators performance when compared to other methods previously proposed in the literature