Abstract

Using freeform optical surfaces is a revolution in the field of imaging system design. Such systems have important applications in the area of virtual reality and augmented reality, light-field and high-performance cameras, microscopy, spectroscopy, and other applied physics researches. We propose a framework of starting points generation for freeform reflective imaging systems using back-propagation (BP) neural network based deep-learning. Good starting points of specific system specifications for optimization can be generated immediately using the network. The amount of time and human effort as well as the dependence on advanced design skills reduce significantly.

© 2019 Japan Society of Applied Physics, The Optical Society (OSA)

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