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

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.