High-throughput biological imaging is often constrained by a trade-off between acquisition speed and image quality. Fast imaging modalities, such as wide-field fluorescence microscopy, enable large-scale data acquisition but suffer from reduced contrast and resolution, whereas high-resolution techniques, including confocal microscopy or single-molecule localization microscopy-based super-resolution techniques, provide superior image quality at the cost of throughput and instrument time. Here, we present a deep learning-based approach for modality transfer across independent microscopes, enabling the transformation of low-quality images acquired on fast systems into high-quality representations comparable to those obtained using advanced imaging platforms. To achieve this, we employ a generative adversarial network (GAN)-based model trained on paired datasets acquired on physically separate wide-field and confocal microscopes, demonstrating that image quality can be reliably transferred between independent instruments. Quantitative evaluation shows substantial improvement in structural similarity and signal fidelity, with median SSIM and PSNR of 0.94 and 31.87, respectively, compared to 0.83 and 21.48 for the original wide-field images. These results indicate that key structural features can be recovered with high accuracy. Importantly, this approach enables a workflow in which high-throughput imaging can be performed on fast, accessible microscopy systems while preserving the ability to computationally recover high-quality structural information. High-resolution microscopy can then be reserved for targeted validation, reducing acquisition time and improving overall experimental efficiency. Together, our results establish deep learning-enabled modality transfer as a practical strategy for bridging independent microscopy systems and supporting scalable, high-content imaging workflows.

翻译：高通量生物成像常受限于采集速度与图像质量之间的权衡。宽场荧光显微镜等快速成像模态虽可实现大规模数据采集，但存在对比度与分辨率降低的问题；而共聚焦显微镜或基于单分子定位超分辨技术的高分辨成像方法，能提供卓越的图像质量，却以牺牲通量与仪器时间为代价。本研究提出一种基于深度学习的跨独立显微镜模态迁移方法，可将快速系统采集的低质量图像转化为与先进成像平台相当的高质量表征。为此，我们采用基于生成对抗网络（GAN）的模型，使用在物理分离的宽场与共聚焦显微镜上采集的配对数据集进行训练，证明图像质量可在独立仪器间可靠迁移。定量评估显示，该方法在结构相似性与信号保真度方面有显著提升，中位数SSIM与PSNR分别达到0.94与31.87，而原始宽场图像仅为0.83与21.48。结果表明关键结构特征可被高精度恢复。重要的是，该方法支持在快速、易用的显微镜系统上进行高通量成像，同时保留通过计算恢复高质量结构信息的能力。高分辨率显微镜可专用于靶向验证，从而缩短采集时间并提高整体实验效率。综合而言，本研究成果将基于深度学习的模态迁移确立为连接独立显微镜系统、支撑可扩展高内涵成像工作流的实用策略。