Affiliation:
1. Shanghai Artificial Intelligence Laboratory
2. Northwestern Polytechnical University
3. Laboratory of Measurement and Sensor System Technique (MST)
4. Competence Center for Biomedical Computational Laser Systems (BIOLAS)
Abstract
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, to the best of our knowledge, open-source dataset tailored for MCF phase imaging, comprising 50,176 paired speckles and phase images. Our trained deep neural network (DNN) demonstrates a 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.
Funder
Shanghai Artificial Intelligence Laboratory
National Key Research and Development Program of China
National Natural Science Foundation of China
Deutsche Forschungsgemeinschaft
Subject
Atomic and Molecular Physics, and Optics