用深度学习加速全息显微镜成像，重建快且准。
OAH-Net: A Deep Neural Network for Hologram Reconstruction of Off-axis Digital Holographic Microscope
- 结合物理原理初始化网络，弱监督微调提升重建精度。
- 相位和振幅图像误差在硬件测量范围内，重建速度超设备采集率。
- 对未见样本泛化能力强，适合生物医学实时分析场景。
离轴数字全息显微镜是一种高通量、无标记的成像技术，可提供样品的三维、高分辨率信息，尤其适用于大规模细胞成像。然而，全息图重建过程成为及时数据分析的主要瓶颈。为此，我们提出一种新型重建方法，将深度学习与离轴全息的物理原理相结合。通过基于物理原理初始化网络部分权重，并采用弱监督超参数学习进行微调，所提出的离轴全息网络（OAH-Net）在相位与振幅图像重建中误差处于硬件测量误差范围内，且重建速度显著超过显微镜采集速率。关键的是，OAH-Net在具有不同特征的未见样本上展现出卓越的外部泛化能力，可无缝集成至下游模型实现端到端实时全息分析。这一能力进一步拓展了离轴全息在生物与医学研究中的应用前景。
原文摘要 · Abstract (English)
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 weakly 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 demonstrates remarkable external generalization capabilities on unseen samples with distinct patterns and can be seamlessly integrated with other models for downstream tasks to achieve 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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