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进步要求具备实时、大视场（FOV）成像能力。传统无透镜成像（LFI）系统虽克服了物理透镜的局限，但受限于动态且难以建模的光场，其单次成像视场仅约20平方毫米。这一限制成为活细胞成像及微流控系统自动化等生物医学研究应用中的主要瓶颈。本文提出一种基于深度学习（DL）的成像框架——GenLFI，利用生成式人工智能（AI）实现全息图像重建。我们证明，GenLFI可实现实时视场超过550平方毫米，较现有LFI系统提升20倍以上，甚至达到全球最大共聚焦显微镜的1.76倍。其分辨率达亚像素级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场建模，使得在复杂光场中对动态三维样本（如液滴微流控和三维细胞模型）进行成像成为可能。该GenLFI框架释放了LFI系统的潜力，为高通量生物医学应用（如药物发现）中的前沿研究提供了强大工具。