Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an
emerging in vivo label-free endoscopic imaging modality with minimal
invasiveness. However, the computational demands of conventional iterative
phase retrieval algorithms have limited their real-time imaging potential. We
demonstrate a learning-based MCF phase imaging method, that significantly
reduced the phase reconstruction time to 5.5 ms, enabling video-rate imaging at
181 fps. Moreover, we introduce an innovative optical system that automatically
generated the first open-source dataset tailored for MCF phase imaging,
comprising 50,176 paired speckle and phase images. Our trained deep neural
network (DNN) demonstrates robust phase reconstruction performance in
experiments with a mean fidelity of up to 99.8\%. Such an efficient fiber phase
imaging approach can broaden the applications of QPI in hard-to-reach areas.

通过多芯光纤实现的定量相位成像（QPI）是一种新兴的无创内窥镜成像模式。我们展示了一种基于学习的 MCF 相位成像方法，将相位重构时间显著缩短至 5.5 毫秒，实现了 181 帧每秒的视频级成像。此外，我们引入了一种创新的光学系统，自动生成了首个针对 MCF 相位成像的开源数据集，包含 50,176 对散斑和相位图像。经过训练的深度神经网络（DNN）在实验中表现出鲁棒的相位重构性能，平均保真度高达 99.8％。这种高效的光纤相位成像方法可以拓宽 QPI 在难以到达区域的应用。