  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.
