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.

翻译：摘要：多色全息图依赖于多个光源的同时照明。这类多色全息图比传统单色全息图能更高效地利用光源，并可提升全息显示器的动态范围。本文提出\projectname——首个用于估算多色全息图照明所需最优光源功率的学习型方法。为此，我们利用合成图像及其深度信息建立了首个多色全息图数据集。这些合成图像通过结合生成模型、大语言模型与单目深度估计模型的流行流水线生成。最终，我们基于该数据集训练学习模型，并通过实验证明：在保证图像质量的前提下，\projectname能够将多色全息图所需的优化步骤从$>1000$步显著降低至70次迭代。