An Unsupervised Optimization of Structuring Elements for Noise Removal Using GA
Hiroyuki OKUNO, Yoshiko Hanada, Mitsuji Muneyasu, Akira Asano · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2010
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.