TY  - GEN
TI  - AutoColor: learned light power control for multi-color holograms
UR  - http://dx.doi.org/10.1117/12.3000082
AV  - public
ID  - discovery10192175
SN  - 0277-786X
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
Y1  - 2024/01/01/
PB  - SPIE
A1  - Zhan, Y
A1  - Kavakll, K
A1  - Urey, H
A1  - Sun, Q
A1  - Ak?it, K
CY  - San Francisco, CA, USA
N2  - 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.
ER  -