用神经网络快速准确检测全息图像中的微米级粒子
FLASHμ: Fast Localizing And Sizing of Holographic Microparticles
- 两阶段神经网络结构，先定位后测尺寸
- 9微米以上粒子检测准确率媲美传统方法，速度提升600倍
- 仅需合成数据训练，适合实时低成本全息成像
从衍射图像（全息图）中重建微粒的三维位置与尺寸是一个计算量大的逆问题，传统上依赖物理模型求解。近年来机器学习方法被用于加速，但在大样本深度（达20厘米）下小粒子（6-100μm）检测性能仍不如传统方法。本文提出两阶段神经网络FLASHμ，仅在含物理噪声的合成数据上训练，可在真实全息图中可靠检测直径≥9μm的粒子，效果接近标准重建方法，且处理区域更小、分辨率仅为原图四分之一，实现约600倍加速。本工作为非局部目标检测与信号分离问题提供了新思路，有望推动低成本、实时全息成像系统的发展。
原文摘要 · Abstract (English)
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$μ$, to detect small particles (6-100$μ$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$μ$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.
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