Côté Geoffroi, Lalonde Jean-François, Thibault Simon
Opt Express. 2021 Feb 1;29(3):3841-3854. doi: 10.1364/OE.401590.
We present a simple, highly modular deep neural network (DNN) framework to address the problem of automatically inferring lens design starting points tailored to the desired specifications. In contrast to previous work, our model can handle various and complex lens structures suitable for real-world problems such as Cooke Triplets or Double Gauss lenses. Our successfully trained dynamic model can infer lens designs with realistic glass materials whose optical performance compares favorably to reference designs from the literature on 80 different lens structures. Using our trained model as a backbone, we make available to the community a web application that outputs a selection of varied, high-quality starting points directly from the desired specifications, which we believe will complement any lens designer's toolbox.
我们提出了一个简单、高度模块化的深度神经网络（DNN）框架，以解决自动推断适合所需规格的透镜设计起点的问题。与之前的工作相比，我们的模型可以处理各种复杂的透镜结构，适用于诸如库克三片式镜头或双高斯镜头等实际问题。我们成功训练的动态模型可以推断出具有实际玻璃材料的透镜设计，其光学性能与文献中80种不同透镜结构的参考设计相比具有优势。以我们训练好的模型为基础，我们向社区提供了一个网络应用程序，该程序可直接根据所需规格输出一系列多样的高质量起点，我们相信这将补充任何透镜设计师的工具包。
