Kavakli, K;
Urey, H;
Akşit, K;
(2022)
Learned holographic light transport: Invited.
Applied Optics
, 61
(5)
B50-B55.
10.1364/AO.439401.
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Abstract
Computer-generated holography 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.
| Type: | Article |
|---|---|
| Title: | Learned holographic light transport: Invited |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/AO.439401 |
| Publisher version: | https://doi.org/10.1364/AO.439401 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10140353 |
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