Evaluation of deep learning-generated lens design starting points

摘要

Data-driven approaches to lens design have only recently begun to emerge. One particular way in which machine learning, and more particularly deep learning, was applied to lens design was by smoothly extrapolating from lens design databases to provide high-quality starting points for lens designers. This mechanism is used by the web application LensNet (which will be publicly available shortly) whose goal is to provide high-quality starting points that are tailored to the desired specifications, namely the effective focal length, f-number and half field of view. Here, we evaluate more thoroughly the designs that are inferred by LensNet and its underlying deep neural network. We provide a global quantitative assessment of the viability of the designs as well as a more targeted comparison among specific design families such as Cooke triplets and Double-Gauss lenses between expert-designed lenses and their automatically inferred counterparts.