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.

翻译：高通量生物医学应用的发展要求具备实时、大视野（FOV）成像能力。传统无透镜成像系统虽突破了物理透镜的限制，却受困于动态且难以建模的光学场，导致其单次成像视野仅约20平方毫米，这已成为活细胞成像、微流控系统自动化等生物医学研究的重大瓶颈。我们提出基于深度学习（DL）的GenLFI成像框架，利用生成式人工智能（AI）实现全息图像重建。实验证明，GenLFI可将实时视野提升至550平方毫米以上，较现有无透镜成像系统提高20余倍，甚至达到全球最大共聚焦显微镜的1.76倍。其分辨率达到亚像素级5.52微米，且无需移动光源。基于无监督学习的重建过程无需光学场建模，使动态三维样本（如液滴微流控与三维细胞模型）在复杂光学场中的成像成为可能。GenLFI框架释放了无透镜成像系统的潜力，为攻克高通量生物医学应用新前沿（如药物发现）提供了有力工具。