End-to-end physics-informed deep neural network optimization of sub-Nyquist lenses.

In this paper, an approach for optimizing sub-Nyquist lenses using an end-to-end physics-informed deep neural network is presented. The simulation and optimization of these sub-Nyquist lenses is investigated for image quality, classification performance, or both. This approach integrates a diffractive optical model with a deep learning classifier, forming a unified optimization framework that facilitates simultaneous simulation and optimization.