Learned holographic light transport
Abstract
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.
Cite
@article{arxiv.2108.08253,
title = {Learned holographic light transport},
author = {Koray Kavaklı and Hakan Urey and Kaan Akşit},
journal= {arXiv preprint arXiv:2108.08253},
year = {2022}
}
Comments
11 pages. Corrected a typo in equation 3