An Unsupervised Optimization of Structuring Elements for Noise Removal Using GA | Zendy

Hiroyuki Okuno | Zendy; Yoshiko Hanada | Zendy; Mitsuji Muneyasu | Zendy; Akira Asano | Zendy

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An Unsupervised Optimization of Structuring Elements for Noise Removal Using GA

Author(s) -

Hiroyuki Okuno,

Yoshiko Hanada,

Mitsuji Muneyasu,

Akira Asano

Publication year - 2010

Publication title -

ieice transactions on fundamentals of electronics communications and computer sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.188

H-Index - 52

eISSN - 1745-1337

pISSN - 0916-8508

DOI - 10.1587/transfun.e93.a.2196

Subject(s) - structuring , computer science , impulse noise , noise reduction , genetic algorithm , noise (video) , reduction (mathematics) , pattern recognition (psychology) , impulse (physics) , structuring element , artificial intelligence , algorithm , mathematics , image (mathematics) , mathematical morphology , machine learning , image processing , pixel , physics , geometry , finance , quantum mechanics , economics

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.

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