Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

Liyun Dou; Dan Xu; Hao Chen; Yicheng Liu

doi:10.1117/12.2281560

21 July 2017 Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

Liyun Dou, Dan Xu, Hao Chen, Yicheng Liu

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202I (2017) https://doi.org/10.1117/12.2281560
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Liyun Dou, Dan Xu, Hao Chen, and Yicheng Liu "Image de-noising based on mathematical morphology and multi-objective particle swarm optimization", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202I (21 July 2017); https://doi.org/10.1117/12.2281560

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