Evaluation of deep learning-generated lens design starting points

Geoffroi Côté; Jean-François Lalonde; Simon Thibault

doi:10.1117/12.2570605

29 August 2020 Evaluation of deep learning-generated lens design starting points

Geoffroi Côté, Jean-François Lalonde, Simon Thibault

Proceedings Volume 11482, Current Developments in Lens Design and Optical Engineering XXI; 1148208 (2020) https://doi.org/10.1117/12.2570605
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Geoffroi Côté, Jean-François Lalonde, and Simon Thibault "Evaluation of deep learning-generated lens design starting points", Proc. SPIE 11482, Current Developments in Lens Design and Optical Engineering XXI, 1148208 (29 August 2020); https://doi.org/10.1117/12.2570605

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