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Proceedings Paper

Denoising in spatial particle tomography on multi-layer holography reconstruction by deep learning
Author(s): Jiaxing Li; Xiaoyan Wu; Ketao Yan; Yingjie Yu
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Paper Abstract

Spatial particle distribution can be recorded by holography technology and can be constructed from multi-layer hologram. Due to the influence of holographic recording and reconstruction process, each tomography of multi-layer reconstruction from holography also contains noise in addition to containing spatial particle distribution information. How to denoise each tomography is a key problem. The existing methods either have a long operation time or the noise reduction effect is not obvious. In order to solve the above problems, we proposed a denoising method based on deep learning in this paper. A deep neural network is built to train and test with simulated spatial particle tomography on multi-layer holography reconstruction. According to the simulation results, the method proposed in this paper is effective in denoising the reconstruction results of spatial particles. The proposed method has the advantages of rapidity and high efficiency.

Paper Details

Date Published: 16 October 2019
PDF: 6 pages
Proc. SPIE 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 112051B (16 October 2019); doi: 10.1117/12.2541651
Show Author Affiliations
Jiaxing Li, Shanghai Univ. (China)
Xiaoyan Wu, Shanghai Polytechnic Univ. (China)
Ketao Yan, Shanghai Univ. (China)
Yingjie Yu, Shanghai Univ. (China)

Published in SPIE Proceedings Vol. 11205:
Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019)
Anand Asundi; Motoharu Fujigaki; Huimin Xie; Qican Zhang; Song Zhang; Jianguo Zhu; Qian Kemao, Editor(s)

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