Reconstructing the 3D location and size of microparticles from diffraction images - holograms - is a computationally expensive inverse problem that has traditionally been solved using physics-based reconstruction methods. More recently, researchers have used machine learning methods to speed up the process. However, for small particles in large sample volumes the performance of these methods falls short of standard physics-based reconstruction methods. Here we designed a two-stage neural network architecture, FLASH$\mu$, to detect small particles (6-100$\mu$m) from holograms with large sample depths up to 20cm. Trained only on synthetic data with added physical noise, our method reliably detects particles of at least 9$\mu$m diameter in real holograms, comparable to the standard reconstruction-based approaches while operating on smaller crops, at quarter of the original resolution and providing roughly a 600-fold speedup. In addition to introducing a novel approach to a non-local object detection or signal demixing problem, our work could enable low-cost, real-time holographic imaging setups.

本研究针对传统物理重建方法在小颗粒大样本体积中表现不佳的问题，提出了一种两阶段神经网络架构FLASHμ。该方法能够在合成数据上进行训练，并在真实全息图中稳定地检测到至少9μm直径的颗粒，实现了约600倍的速度提升，将检测过程简化至较小的图像裁剪区域。

FLASHμ：全息微粒的快速定位与尺寸测量