{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T18:38:21Z","timestamp":1792694301509,"version":"4.5.0"},"reference-count":16,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62305246"],"award-info":[{"award-number":["62305246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Deep-learning-based holographic reconstruction requires numerical data with reliable ground truth for training and quantitative evaluation. Experimental hologram acquisition and pixel-level phase annotation can be difficult to control, making physically defined numerical cases useful as reproducible benchmarks. Here, three analytically specified phase targets\u2014a smooth Gaussian distribution, a four-level step distribution, and a radially structured Siemens Star\u2014are used to generate off-axis digital holograms with known phase ground truth. A frequency-domain filtering procedure is applied before inverse Fourier reconstruction, followed by DCT-based global phase unwrapping. Reconstruction quality is quantified using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and profile-based error analysis. The results show that reconstruction errors are comparatively small in smooth regions and increase near abrupt or high-spatial-frequency phase transitions. The three cases are therefore presented as controlled benchmark examples for algorithm verification and baseline comparison, not as a complete substitute for experimental holographic datasets or as a complete training corpus for deep-learning models.<\/jats:p>","DOI":"10.1371\/journal.pone.0360375","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:43:12Z","timestamp":1792690992000},"page":"e0360375","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Controlled numerical generation and reconstruction of off-axis digital holograms with object-specific frequency-domain filtering"],"prefix":"10.1371","volume":"21","author":[{"given":"Junsheng","family":"Lu","sequence":"first","affiliation":[{"name":"School of Life Sciences, Tiangong\u00a0University","place":["Tianjin, 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holographic microscopy for red blood cell imaging","volume":"69","author":"YY Zhang","year":"2020","journal-title":"Acta Phys Sin"},{"issue":"2","key":"pone.0360375.ref009","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/nphoton.2015.279","article-title":"Standardizing the resolution claims for coherent microscopy","volume":"10","author":"R Horstmeyer","year":"2016","journal-title":"Nature Photon"},{"issue":"2","key":"pone.0360375.ref010","first-page":"15","article-title":"Digital holography in production: An overview","volume":"2","author":"M Fratz","year":"2021","journal-title":"Light Adv Manuf"},{"key":"pone.0360375.ref011","doi-asserted-by":"crossref","first-page":"107624","DOI":"10.1016\/j.optlaseng.2023.107624","article-title":"Fast automated quantitative phase reconstruction in digital holography with unsupervised deep learning","volume":"167","author":"S Park","year":"2023","journal-title":"Opt Lasers 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