{
 "id": "SPIN-Lab/KOREATECH-CGH-2048-3.6Mu",
 "owner": "SPIN-Lab",
 "name": "KOREATECH-CGH-2048-3.6Mu",
 "downloads": 3360,
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 "last_modified": "2026-07-08T05:53:10.000Z",
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 "citation": "@article{LEE2026115636,\ntitle = {A large-depth-range layer-based hologram dataset generation for machine learning-based 3D computer-generated holography},\njournal = {Optics & Laser Technology},\nvolume = {203},\npages = {115636},\nyear = {2026},\nissn = {0030-3992},\ndoi = {https://doi.org/10.1016/j.optlastec.2026.115636},\nurl = {https://www.sciencedirect.com/science/article/pii/S0030399226009874},\nauthor = {Jaehong Lee and You Chan No and YoungWoo Kim and Duksu Kim},\nkeywords = {CGH(Computer-generated holography), Hologram, Machine-learning, Dataset, RGB-d, ML-CGH},\nabstract = {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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 "revision": "7934c793b741573acad27a1c249fcac7a80efae2",
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