Challenges using data-driven methods and deep learning in optical engineering

Julie Buquet; Jocelyn Parent; Jean-François Lalonde; Simon Thibault

doi:10.1117/12.2636262

3 October 2022 Challenges using data-driven methods and deep learning in optical engineering

Julie Buquet, Jocelyn Parent, Jean-François Lalonde, Simon Thibault

Proceedings Volume 12217, Current Developments in Lens Design and Optical Engineering XXIII; 122170E (2022) https://doi.org/10.1117/12.2636262
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Data driven approaches have proven very efficient in many vision tasks and are now used for optical parameters optimization in application-specific camera design. A neural network is trained to estimate images or image quality indicators from the optical characteristics. The complexity and entanglement of such optical parameters raise new challenges we investigate in the case of wide-angle systems. We highlight them by establishing a data-driven prediction model of the RMS spot size from the distortion using mathematical or AI-based methods.

Conference Presentation

Citation Download Citation

Julie Buquet, Jocelyn Parent, Jean-François Lalonde, and Simon Thibault "Challenges using data-driven methods and deep learning in optical engineering", Proc. SPIE 12217, Current Developments in Lens Design and Optical Engineering XXIII, 122170E (3 October 2022); https://doi.org/10.1117/12.2636262

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