Abstract
We report a computer-free imaging framework in which a set of transmissive diffractive layers were trained using deep learning to all-optically reconstruct arbitrary objects hidden by unknown, random phase diffusers. The image reconstruction of the object hidden behind a random and unknown phase diffuser is completed at the speed of light propagation through a thin, engineered diffractive volume. Our analyses provide a comprehensive guide for designing robust and generalizable diffractive imagers to all-optically see through random diffusers, which might be transformative for various fields, such as biomedical imaging, atmospheric physics, and autonomous driving.
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