  Computer-Generated Holography (CGH) algorithms often fall short in matching
simulations with results from a physical holographic display. Our work
addresses this mismatch by learning the holographic light transport in
holographic displays. Using a camera and a holographic display, we capture the
image reconstructions of optimized holograms that rely on ideal simulations to
generate a dataset. Inspired by the ideal simulations, we learn a
complex-valued convolution kernel that can propagate given holograms to
captured photographs in our dataset. Our method can dramatically improve
simulation accuracy and image quality in holographic displays while paving the
way for physically informed learning approaches.
