Digital Holography and Microscopy
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Abstract
Abstract
Digital holography, which enables quantitative phase imaging, suffers from reconstruction degradation caused by environmental noise and system imperfections. We propose a digital twin-inspired deep learning framework for high-fidelity reconstruction of digital holograms, in which a physics-based simulation of the recording system is used to generate training data in a virtual domain. This approach alleviates the dependence on ideal optical setups and strictly controlled experimental conditions. We further investigate supervised, unsupervised, and self-supervised learning schemes for high-fidelity reconstruction of digital holograms recorded from phase objects. Experimental results demonstrate that the proposed framework improves reconstruction fidelity under realistic experimental conditions.
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@article{Esaki2026Digital,
title = {Digital twin-inspired deep learning for high-fidelity reconstruction of digital holograms},
author = {R. Esaki and Masanori Takabayashi},
journal = {Optical Review},
year = {2026},
doi = {10.1007/s10043-026-01070-6},
url = {https://doi.org/10.1007/s10043-026-01070-6}
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