OAH-Net: A Deep Neural Network for Efficient and Robust Hologram Reconstruction for Off-axis Digital Holographic Microscopy
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Posted on 2025-02-04 - 14:15
Off-axis digital holographic microscopy is a high-throughput, label-free imaging
technology that provides three-dimensional, high-resolution information about samples, particularly useful in large-scale cellular imaging. However, the hologram reconstruction process
poses a significant bottleneck for timely data analysis. To address this challenge, we propose
a novel reconstruction approach that integrates deep learning with the physical principles of
off-axis holography. We initialized part of the network weights based on the physical principle
and then fine-tuned them via supersized learning. Our off-axis hologram network (OAH-Net)
retrieves phase and amplitude images with errors that fall within the measurement error range
attributable to hardware, and its reconstruction speed significantly surpasses the microscope’s
acquisition rate. Crucially, OAH-Net, trained and validated on diluted whole blood samples,
demonstrates remarkable external generalization capabilities on unseen samples with distinct
patterns. Additionally, it can be seamlessly integrated with other models for downstream tasks,
enabling end-to-end real-time hologram analysis. This capability further expands off-axis
holography’s applications in both biological and medical studies.
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Liu, Wei; Delikoyun, Kerem; Chen, Qianyu; Yildiz, Alperen; Myo, Si Ko; Kuan, Win Sen; et al. (2025). OAH-Net: A Deep Neural Network for Efficient and Robust Hologram Reconstruction for Off-axis Digital Holographic Microscopy. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.7526685.v2