Research On Bionic Algorithms-based Soft Morphological Filters Optimization

Soft morphology is an important subclass of development of mathematical morphology. In 1991, L. Koskinen introduced soft morphological filters. The key idea of soft morphological filters is that the structuring set is divided into two parts: the "hard center" and the "soft boundary" where maximum and minimum are replaced by other order statistics. This makes the filter more tolerant to noise and makes the filter less sensitive to additive noise and small variations in the shapes of the objects to be filtered. In this paper, some researches of optimal algorithms and its application of soft morphological filters have been made as fellow.The basic theory and concept of morphology and soft morphological filter are introduced systemically. Different properties of soft morphological filters are illustrated. It is shown that soft morphological filters are with many desirable properties. The experimental results of remote sensing image processing illustrat -ed that soft morphological filters can preserve well details of image while filtering the noise.This paper focuses on the research of optimal algorithms for soft morpholo -gical filter design. Under MSE and MAE criteria, this paper proposes three effective and easy-to-be implemented algorithms for optimization of soft morphological filters, i.e. genetic algorithms, tabu search algorithm and ant colony algorithm. During the optimization based on genetic algorithm, this paper simulates the evolution rules of the natural species, adopting the subsection adaptive crossover rate and mutation rate, using the mechanism of simulated annealing algorithm to control the populations' selection, introducing the eugenics principle to strengthen the potential advantage, and describes the optimization parameters with the structure of neural networks; During the optimization based on tabu search algorithm, this paper partly simulates the reasoning and ideation ofthe brain, setting tabu list and tabu tenure reasonably to avoid the search to fall into local optimum, making the full use of the algorithm's special structure to achieve the global optimal solution; During the optimization based on ant colony algorithm, this paper simulates the communication and cooperation process when the natural ants look for food, setting the parameters of artificial ant colony, utilizing the parallel calculation positive feedback and probability selection ability of the algorithm, adjusting randomicity and astringency of the search, and realizes the multi-parameter combined optimization of soft morphological filter. Their feasibility and effectiveness are all proved by results of simulated experiments.