物理驱动的数字同轴全息显微术通用孪生像消除网络
Physics-driven universal twin-image removal network for digital in-line holographic microscopy
AI总结：
提出UTIRnet，一种仅用数值数据训练的深度学习网络，通过物理一致性约束实现快速、通用且可靠的数字同轴全息显微术孪生像消除，并在活细胞迁移实验中验证。
摘要：
数字同轴全息显微术（DIHM）能够实现大视场下高效且成本低廉的计算定量相位成像，使其在研究细胞运动、迁移和生物微流控方面具有重要价值。然而，DIHM重建质量受到孪生像噪声的影响，这构成了重大挑战。缓解该噪声的传统方法涉及复杂的硬件设置或耗时的算法，且效果往往有限。在本工作中，我们提出了UTIRnet，一种用于快速、稳健且普遍适用的孪生像抑制的深度学习解决方案，该网络仅使用数值生成的数据集进行训练。开源UTIRnet代码的可用性使其能够方便地部署于各种DIHM系统，而无需大量实验训练数据。值得注意的是，我们的网络确保了重建结果与输入全息图的一致性，赋予了基于物理的基础，相比传统深度学习方法增强了可靠性。实验验证在活体神经胶质细胞培养迁移传感等方面进行，这对神经退行性疾病研究至关重要。
英文摘要：
Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
