SPIN-Lab/KOREATECH-CGH-1024-3.6Mu

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@article{LEE2026115636,
title = {A large-depth-range layer-based hologram dataset generation for machine learning-based 3D computer-generated holography},
journal = {Optics & Laser Technology},
volume = {203},
pages = {115636},
year = {2026},
issn = {0030-3992},
doi = {https://doi.org/10.1016/j.optlastec.2026.115636},
url = {https://www.sciencedirect.com/science/article/pii/S0030399226009874},
author = {Jaehong Lee and You Chan No and YoungWoo Kim and Duksu Kim},
keywords = {CGH(Computer-generated holography), Hologram, Machine-learning, Dataset, RGB-d, ML-CGH},
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 6000 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 27.46 dB PSNR and 0.87 SSIM, surpassing a recent optimized silhouette-masking layer-based method by 3.86 dB and 0.09 SSIM, respectively. We fu

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dataset_licence({ "id": "SPIN-Lab/KOREATECH-CGH-1024-3.6Mu" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0