Abstract
In this paper, we propose a method to automatically generate design starting points for free-form three-mirror imaging systems with different folding configurations using deep neural networks. For a given range of system parameters, a large number of datasets are automatically generated using the double seed extended curve algorithm and coded optimization. Deep neural networks are then trained using a supervised learning approach and can be used to generate good design starting points directly. The feasibility of the method is verified by designing a free-form three-mirror system with three different folding configurations. This method can significantly reduce the design time and effort for free-form imaging systems, and can be extended to complex optical systems with more optical surfaces.
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Full Article | PDF ArticleRetraction
This article has been retracted. Please see:Chengxiang Fan, Bo Yang, Yunpeng Liu, Qianyang Zhao, Shishuang Chen, and Bowen Qian, "Using deep learning to automatically generate design starting points for free-form imaging optical systems: retraction," Appl. Opt. 62, 5889-5889 (2023)
https://opg.optica.org/ao/abstract.cfm?uri=ao-62-22-5889
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