Title: Machine Learning Holography for 3D Particle Field Imaging Show Chinese title

Title: 机器学习全息术用于三维粒子场成像

Abstract: 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. Show Chinese abstract

Abstract: 我们提出了一种基于学习的新方法，用于使用全息术的3D粒子场成像。 我们的方法使用了一个结合残差连接、Swish激活函数、全息图预处理和迁移学习的U-net架构，以应对在粒子全息图中准确测量单个粒子所带来的挑战。 对合成和实验全息图的评估表明，与之前的方法相比，在广泛的粒子浓度范围内，包括高密度浓度的情况，粒子提取率、定位精度和速度都有显著提高。 我们的方法可以潜在地扩展到其他具有类似特征的计算成像任务。