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.
翻译：基于机器学习的计算机生成全息术近年来发展迅速，但其进展受限于高质量、大规模全息图数据集的稀缺。为此，我们提出了KOREATECH-CGH，一个公开可用的数据集，包含6,000对RGB-D图像与复振幅全息图，分辨率覆盖256*256至2048*2048，其深度范围扩展至角谱法理论极限，以实现广泛的3D场景覆盖。为提升大深度范围下的全息图质量，我们引入了振幅投影技术，这是一种后处理方法，可在保持相位不变的同时替换全息图波场在各深度层的振幅分量。该方法提升了重建保真度，实现了27.01 dB的峰值信噪比和0.87的结构相似性指数，分别优于近期一种优化的基于轮廓掩模分层方法2.03 dB和0.04。我们进一步通过使用前沿机器学习模型进行全息图生成和超分辨率实验，验证了KOREATECH-CGH的实用性，确认了其适用于训练和评估下一代基于机器学习的计算机生成全息系统。
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