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Proceedings Paper

Fast measurement of mid-spatial-frequency error on optical surfaces with convolutional neural networks
Author(s): Jing Wang; Jian Bai; Xiao Huang; Jing Hou
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Paper Abstract

Mid-spatial-frequency (MSF) error on optical surfaces can do great harm to high-performance laser systems. A non-interferometric way of measuring it is phase retrieval, which has already proved its effectiveness in previous studies. However, the performance of phase retrieval is limited by its long-time iterative process and relies heavily on reliable initial solution. Therefore, in this paper, we put forward a method for fast measurement of MSF error, by introducing advanced deep learning technique into traditional computational imaging methods. Results show that the proposed method simultaneously gains an improvement on convergence speed and a reduction on residual error. The proposed method takes much fewer iterations to converge to the same error level, and has much smaller average residual error than that of the conventional algorithm in the numerical experiments.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134205 (18 December 2019); doi: 10.1117/12.2541988
Show Author Affiliations
Jing Wang, Zhejiang Univ. (China)
Jian Bai, Zhejiang Univ. (China)
Xiao Huang, Zhejiang Univ. (China)
Jing Hou, Research Ctr. of Laser Fusion (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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