AutoColor: learned light power control for multi-color holograms

Yicheng Zhan; Koray Kavaklı; Hakan Urey; Qi Sun; Kaan Akşit

doi:10.1117/12.3000082

12 March 2024 AutoColor: learned light power control for multi-color holograms

Yicheng Zhan, Koray Kavaklı, Hakan Urey, Qi Sun, Kaan Akşit

Proceedings Volume 12913, Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V; 129130I (2024) https://doi.org/10.1117/12.3000082
Event: SPIE AR | VR | MR, 2024, San Francisco, California, United States

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yicheng Zhan, Koray Kavaklı, Hakan Urey, Qi Sun, and Kaan Akşit "AutoColor: learned light power control for multi-color holograms", Proc. SPIE 12913, Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V, 129130I (12 March 2024); https://doi.org/10.1117/12.3000082

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