We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.

中文翻译：

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用于3D粒子场成像的机器学习全息术。

我们提出了一种新的基于学习的使用全息术进行3D粒子场成像的方法。我们的方法使用U-net架构，该架构结合了残留连接，Swish激活，全息图预处理和转移学习，以应对粒子全息图中出现的挑战，其中精确测量单个粒子至关重要。对合成全息图和实验全息图的评估均表明，与现有方法相比，在较宽的颗粒浓度范围内，包括其他方法均不适用的高密度浓度条件下，颗粒提取率，定位精度和速度均得到了显着改善。我们的方法可以潜在地扩展到具有类似功能的其他类型的计算成像任务。