Abstract
Machine learning-based computer-generated holography (ML-CGH) has advanced rapidly in recent years, yet progress is constrained by the limited availability of high-quality, large-scale hologram datasets. To address this, we present KOREATECH-CGH, a publicly available dataset comprising 6,000 pairs of RGB-D images and complex holograms across resolutions ranging from 256*256 to 2048*2048, with depth ranges extending to the theoretical limits of the angular spectrum method for wide 3D scene coverage. To improve hologram quality at large depth ranges, we introduce amplitude projection, a post-processing technique that replaces amplitude components of hologram wavefields at each depth layer while preserving phase. This approach enhances reconstruction fidelity, achieving 27.01 dB PSNR and 0.87 SSIM, surpassing a recent optimized silhouette-masking layer-based method by 2.03 dB and 0.04 SSIM, respectively. We further validate the utility of KOREATECH-CGH through experiments on hologram generation and super-resolution using state-of-the-art ML models, confirming its applicability for training and evaluating next-generation ML-CGH systems.
Abstract (translated)
基于机器学习的计算机全息技术（ML-CGH）近年来取得了迅速的发展，然而其进步受到高质量、大规模全息图数据集稀缺性的限制。为了解决这个问题，我们提出了KOREATECH-CGH，这是一个公开可用的数据集，包含6,000对不同分辨率的RGB-D图像和复数全息图（从256*256到2048*2048），深度范围扩展到了角谱法理论极限，以实现广泛的三维场景覆盖。为了在大深度范围内提高全息图的质量，我们引入了一种后处理技术——振幅投影，在每个深度层中用相位保持不变的方式替换全息波场中的振幅分量。这种方法增强了重建的保真度，PSNR达到了27.01 dB，SSIM为0.87，分别超过了最近优化的轮廓掩膜层方法2.03 dB和0.04 SSIM。通过使用最先进的ML模型进行全息图生成和超分辨率实验，我们进一步验证了KOREATECH-CGH数据集的应用价值，证明其适用于下一代ML-CGH系统的训练和评估。
URL
https://arxiv.org/abs/2512.21040
