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Proceedings Paper

Research on BP neural network for terahertz image segmentation
Author(s): Yu-Tong Wang; Qi Li; Yue Wang
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Paper Abstract

Terahertz digital holographic reconstructed images are vulnerable to noise pollution. This paper uses neural network to segment terahertz image, because this method is insensitive to noise. Firstly, the training sample image is decomposed into several sub-images, and the backward propagation(BP) neural network is trained by them. At the same time, the optimal number of hidden layer neurons is selected. Then the trained neural network is applied to the segmentation of terahertz image. Different segmentation results are obtained by changing the variance of noise in the training sample image. The best segmentation results and training samples are determined by using the mean structural similarity(MSSIM). Finally, compared with the classical image segmentation algorithm, the results show that the segmentation effect of the neural network is better.

Paper Details

Date Published: 20 December 2019
PDF: 6 pages
Proc. SPIE 11209, Eleventh International Conference on Information Optics and Photonics (CIOP 2019), 112091N (20 December 2019); doi: 10.1117/12.2547541
Show Author Affiliations
Yu-Tong Wang, Harbin Institute of Technology (China)
Qi Li, Harbin Institute of Technology (China)
Yue Wang, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 11209:
Eleventh International Conference on Information Optics and Photonics (CIOP 2019)
Hannan Wang, Editor(s)

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