Controlled numerical generation and reconstruction of off-axis digital holograms with object-specific frequency-domain filtering
- 1School of Life Sciences, Tiangong University, Tianjin, China.
- 2College of Engineering and Technology, Tianjin Agricultural University, Tianjin, China.
Abstract
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—a smooth Gaussian distribution, a four-level step distribution, and a radially structured Siemens Star—are 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.
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BibTeX
@article{lu2026controlled,
title = {{Controlled numerical generation and reconstruction of off-axis digital holograms with object-specific frequency-domain filtering}},
author = {Junsheng Lu and Xiaolong Shi and Jiaqi Sun and Shuhong Ren and Yanan Zeng},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0360375},
url = {https://doi.org/10.1371/journal.pone.0360375},
note = {CC BY},
}
RIS
TY - JOUR
TI - Controlled numerical generation and reconstruction of off-axis digital holograms with object-specific frequency-domain filtering
AU - Junsheng Lu
AU - Xiaolong Shi
AU - Jiaqi Sun
AU - Shuhong Ren
AU - Yanan Zeng
PY - 2026
DA - 2026/10/22
JO - PLOS One
DO - 10.1371/journal.pone.0360375
UR - https://doi.org/10.1371/journal.pone.0360375
AB - 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—a smooth Gaussian distribution, a four-level step distribution, and a radially structured Siemens Star—are 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.
SN - 1932-6203
ER -