Generating off-axis reflective imaging systems consisting of flat phase elements based on deep learning

Boyu Mao; Tong Yang; Huiming Xu; Dewen Cheng; Yongtian Wang

doi:10.1117/12.2686762

28 November 2023 Generating off-axis reflective imaging systems consisting of flat phase elements based on deep learning

Boyu Mao, Tong Yang, Huiming Xu, Dewen Cheng, Yongtian Wang

Author Affiliations +

Proceedings Volume 12765, Optical Design and Testing XIII; 127651E (2023) https://doi.org/10.1117/12.2686762
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Imaging systems consisting of flat phase elements can achieve more compactness and lighter-weight. In this paper, we propose a design framework of off-axis reflective imaging system consisting of flat phase elements based on deeplearning. Differential ray tracing for off-axis systems consisting of flat phase elements is used. Supervised and unsupervised learning are combined to improve the generalization ability of the deep neural network for a wide range of system and structure parameter values. Single or multiple systems can be generated directly after the design requirements are inputted into the network, and can be taken as good starting points for further optimization. The design efficiency can be significantly improved, and the dependence on the advanced design skills is dramatically reduced.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Boyu Mao, Tong Yang, Huiming Xu, Dewen Cheng, and Yongtian Wang "Generating off-axis reflective imaging systems consisting of flat phase elements based on deep learning", Proc. SPIE 12765, Optical Design and Testing XIII, 127651E (28 November 2023); https://doi.org/10.1117/12.2686762

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