Generative deep learning-enabled ultra-large field-of-view lens-free imaging
<|reference_start|>Generative deep learning-enabled ultra-large field-of-view lens-free imaging: 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.<|reference_end|>
@article{liu2024generative,
    title={Generative deep learning-enabled ultra-large field-of-view lens-free
  imaging},
    author={Ronald B. Liu, Zhe Liu, Max G.A. Wolf, Krishna P. Purohit, Gregor
  Fritz, Yi Feng, Carsten G. Hansen, Pierre O. Bagnaninchi, Xavier Casadevall i
  Solvas, Yunjie Yang},
    journal={arXiv preprint arXiv:2403.07786},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07786},
    primaryClass={physics.optics cs.CV}
}