Fast measurement of mid-spatial-frequency error on optical surfaces with convolutional neural networks

Jing Wang; Jian Bai; Xiao Huang; Jing Hou

doi:10.1117/12.2541988

18 December 2019 Fast measurement of mid-spatial-frequency error on optical surfaces with convolutional neural networks

Jing Wang, Jian Bai, Xiao Huang, Jing Hou

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 1134205 (2019) https://doi.org/10.1117/12.2541988
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

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.

Citation Download Citation

Jing Wang, Jian Bai, Xiao Huang, and Jing Hou "Fast measurement of mid-spatial-frequency error on optical surfaces with convolutional neural networks", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134205 (18 December 2019); https://doi.org/10.1117/12.2541988

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