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An Unsupervised Optimization of Structuring Elements for Noise Removal Using GA

Hiroyuki OKUNO, Yoshiko HANADA, Mitsuji MUNEYASU, Akira ASANO

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Summary :

In this paper we propose an unsupervised method of optimizing structuring elements (SEs) used for impulse noise reduction in texture images through the opening operation which is one of the morphological operations. In this method, a genetic algorithm (GA), which can effectively search wide search spaces, is applied and the size of the shape of the SE is included in the design variables. Through experiments, it is shown that our new approach generally outperforms the conventional method.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.11 pp.2196-2199
Publication Date
2010/11/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E93.A.2196
Type of Manuscript
Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)
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