Effectiveness of Genetic Multi-Step Search on Unsupervised Design of Morphological Filters for Noise Removal

概要

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This paper shows the effectiveness of deterministic Multi-step Crossover Fusion (dMSXF) on an unsupervised design problem of suitable structuring elements (SEs) of a morphological filter. In our previous work, it was shown that dMSXF worked very well for solving combinatorial optimization problems, especially on problems for which the landscape is an AR(1) landscape observed in the NK model. In addition, the effectiveness on reproduction mechanisms of offspring of dMSXF was shown to be kept through increases in the 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 the superior search performance of dMSXF to a conventional crossover is shown. The processing results of the obtained SEs are also compared to that of a conventional filter for impulse noise removal.
一般社団法人情報処理学会の論文
2010-05-14