Effectiveness of Genetic Multistep Search in Unsupervised Design of Morphological Filters for Noise Removal

Abstract

In this paper, the effectiveness of deterministic Multi-step Crossover Fusion (dMSXF) and deterministic Multi-step Mutation Fusion (dMSMF), which are types of genetic multistep searches based on a neighborhood search mechanism, in solving an unsupervised design problem of suitable structuring elements (SEs) of a morphological filter is shown. In our previous work, it was shown that dMSXF and dMSMF are very effective for solving combinatorial optimization problems, particularly on problems for which the landscape is an AR(1) landscape observed in the NK model. In addition, their effectiveness for reproduction mechanisms to obtain the offspring was shown to be retained with increasing level of epistasis. In this paper, we show that a characteristic of the AR(1) landscape is observed in an objective function for the unsupervised design of SEs, and superior search performances of both dMSXF and dMSMF for conventional crossover are shown. The processing results of the obtained SEs are also compared with those of conventional filters used for impulse noise removal.