Related papers: Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning

Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning

URL: http://arxiv.org/abs/2312.07102v1
Date: Tue, 12 Dec 2023 09:30:12 GMT
Title: Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Authors: Jiawei Sun, Bin Zhao, Dong Wang, Zhigang Wang, Jie Zhang, Nektarios Koukourakis, Juergen W. Czarske, Xuelong Li
Abstract summary: We demonstrate a learning-based MCF phase imaging method, that significantly reduced the phase reconstruction time to 5.5 ms. We also introduce an innovative optical system that automatically generated the first open-source dataset tailored for MCF phase imaging. Our trained deep neural network (DNN) demonstrates robust phase reconstruction performance in experiments with a mean fidelity of up to 99.8%.
Score: 49.013721992323994
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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 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.