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AutoColor: learned light power control for multi-color holograms

Zhan, Y; Kavakll, K; Urey, H; Sun, Q; Akşit, K; (2024) AutoColor: learned light power control for multi-color holograms. In: Hua, Hong and Argaman, Naamah and Nikolov, Daniel K, (eds.) Proceedings of SPIE - The International Society for Optical Engineering. (pp. 129130I). SPIE: San Francisco, CA, USA. Green open access

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Abstract

Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor, the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that AutoColor significantly decreases the number of steps required to optimize multi-color holograms from > 1000 to 70 iteration steps without compromising image quality.

Type: Proceedings paper
Title: AutoColor: learned light power control for multi-color holograms
Event: Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V
Dates: 29 Jan 2024 - 1 Feb 2024
ISBN-13: 9781510670860
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.3000082
Publisher version: http://dx.doi.org/10.1117/12.3000082
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.
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-pp.ucl.ac.uk/id/eprint/10192175
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