Share Email Print
cover

Proceedings Paper

Image de-noising based on mathematical morphology and multi-objective particle swarm optimization
Author(s): Liyun Dou; Dan Xu; Hao Chen; Yicheng Liu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202I (21 July 2017); doi: 10.1117/12.2281560
Show Author Affiliations
Liyun Dou, Yunnan Univ. (China)
Dan Xu, Yunnan Univ. (China)
Hao Chen, Yunnan Univ. (China)
Yicheng Liu, Yunnan Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top