@inproceedings{3ddc033bc9fe4a699484156be613f3eb,
title = "AutoColor: learned light power control for multi-color holograms",
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.",
keywords = "Computer Generated Holography, Computer Graphics, Machine Learning",
author = "Yicheng Zhan and Koray Kavakll and Hakan Urey and Qi Sun and Kaan Ak{\c s}it",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V 2024 ; Conference date: 29-01-2024",
year = "2024",
doi = "10.1117/12.3000082",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Naamah Argaman and Hong Hua and Nikolov, {Daniel K.}",
booktitle = "Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V",
}