Zheng, C;
Zhan, Y;
Akşit, K;
(2025)
Learning plane-to-multiplane light propagation improves hologram optimization.
In: Yoshikawa, Hiroshi and Blanche, Pierre-Alexandre J, (eds.)
Proceedings Volume 13390, Practical Holography XXXIX: Displays, Materials, and Applications; 1339006 (2025).
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Abstract
Computer-Generated Holography (CGH) reconstructs Three-Dimensional (3D) scene by encoding information into holograms. Traditional CGH algorithms decompose the 3D scenes into multiple planes at different depth levels and simulate light propagation between these planes. However, conventional light propagation methods used in CGH are limited to plane-to-plane simulations, which may increase computational demands when a 3D scene is represented with numerous successive planes. We introduce a novel learned model that simulates light propagation from a single hologram plane to multiple planes in a single forward pass. In this way, our method can help reduce the computational complexity of optimizing 3D holograms in CGH algorithms.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning plane-to-multiplane light propagation improves hologram optimization |
| Event: | Practical Holography XXXIX: Displays, Materials, and Applications |
| Dates: | 25 Jan 2025 - 31 Jan 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.3040869 |
| Publisher version: | https://doi.org/10.1117/12.3040869 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computational-Generated Holography, Light Propagation, Convolutional Neural Networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10208487 |
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