Advancements in high-throughput biomedical applications require real-time, large field-of-view (FOV) imaging. While current 2D lens-free imaging (LFI) systems improve FOV, they are often hindered by time-consuming multi-position measurements, extensive data pre-processing, and strict optical parameterization, limiting their application to static, thin samples. To overcome these limitations, we introduce GenLFI, combining a generative unsupervised physics-informed neural network (PINN) with a large FOV LFI setup for straightforward holographic image reconstruction, without multi-measurement. GenLFI enables real-time 2D imaging for 3D samples, such as droplet-based microfluidics and 3D cell models, in dynamic complex optical fields. Unlike previous methods, our approach decouples the reconstruction algorithm from optical setup parameters, enabling a large FOV limited only by hardware. We demonstrate a real-time FOV exceeding 550 mm2, over 20 times larger than current real-time LFI systems. This framework unlocks the potential of LFI systems, providing a robust tool for advancing automated high-throughput biomedical applications.
@article{arxiv.2403.07786,
title = {Physics-informed generative real-time lens-free imaging},
author = {Ronald B. Liu and Zhe Liu and Max G. A. Wolf and Krishna P. Purohit and Gregor Fritz and Yi Feng and Carsten G. Hansen and Pierre O. Bagnaninchi and Xavier Casadevall i Solvas and Yunjie Yang},
journal= {arXiv preprint arXiv:2403.07786},
year = {2025}
}