FLASHμ: Fast Localizing And Sizing of Holographic Microparticles
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
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.
