  Advancements in high-throughput biomedical applications necessitate
real-time, large field-of-view (FOV) imaging capabilities. Conventional
lens-free imaging (LFI) systems, while addressing the limitations of physical
lenses, have been constrained by dynamic, hard-to-model optical fields,
resulting in a limited one-shot FOV of approximately 20 $mm^2$. This
restriction has been a major bottleneck in applications like live-cell imaging
and automation of microfluidic systems for biomedical research. Here, we
present a deep-learning(DL)-based imaging framework - GenLFI - leveraging
generative artificial intelligence (AI) for holographic image reconstruction.
We demonstrate that GenLFI can achieve a real-time FOV over 550 $mm^2$,
surpassing the current LFI system by more than 20-fold, and even larger than
the world's largest confocal microscope by 1.76 times. The resolution is at the
sub-pixel level of 5.52 $\mu m$, without the need for a shifting light source.
The unsupervised learning-based reconstruction does not require optical field
modeling, making imaging dynamic 3D samples (e.g., droplet-based microfluidics
and 3D cell models) in complex optical fields possible. This GenLFI framework
unlocks the potential of LFI systems, offering a robust tool to tackle new
frontiers in high-throughput biomedical applications such as drug discovery.
