21 July 2017 Image de-noising based on mathematical morphology and multi-objective particle swarm optimization
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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.
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Liyun Dou, Liyun Dou, Dan Xu, Dan Xu, Hao Chen, Hao Chen, Yicheng Liu, 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); doi: 10.1117/12.2281560; https://doi.org/10.1117/12.2281560
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