Affiliation:
1. University of Shanghai for Science and Technology
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.
Funder
National Key Research and Development Program of China
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering
Cited by
4 articles.
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