Côté Geoffroi, Lalonde Jean-François, Thibault Simon
Opt Express. 2019 Sep 30;27(20):28279-28292. doi: 10.1364/OE.27.028279.
We propose for the first time a deep learning approach in assisting lens designers to find a lens design starting point. Using machine learning, lens design databases can be expanded in a continuous way to produce high-quality starting points from various optical specifications. A deep neural network (DNN) is trained to reproduce known forms of design (supervised training) and to jointly optimize the optical performance (unsupervised training) for generalization. In this work, the DNN infers high-performance cemented and air-spaced doublets that are tailored to diverse desired specifications after being fed with reference designs from the literature. The framework can be extended to lens systems with more optical surfaces.
我们首次提出一种深度学习方法，以协助镜片设计师找到镜片设计的起点。利用机器学习，可以持续扩展镜片设计数据库，以便根据各种光学规格生成高质量的起点。训练一个深度神经网络（DNN）来重现已知的设计形式（监督训练），并共同优化光学性能（无监督训练）以实现泛化。在这项工作中，DNN在输入文献中的参考设计后，推断出针对各种所需规格定制的高性能胶合和空气间隔双合透镜。该框架可扩展到具有更多光学表面的透镜系统。
