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 \projectname, 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 \projectname significantly decreases the number of steps required to optimize multi-color holograms from $>1000$ to $70$ iteration steps without compromising image quality.

本文介绍了\projectname——第一种学习方法，用于估计照明多色全息图所需的最佳光源功率，并利用合成图像和深度信息建立了第一个多色全息图数据集。通过使用我们的数据集训练所学模型，实验表明\projectname可以在不损害图像质量的情况下将优化多色全息图所需的步骤数从1000个以上减少到70个。

AutoColor：用于多色全息图的学习型光功率控制