| Autor: |
Sun J, Zhao B, Wang D, Wang Z, Zhang J, Koukourakis N, Czarske JW, Li X |
| Jazyk: |
angličtina |
| Zdroj: |
Optics letters [Opt Lett] 2024 Jan 15; Vol. 49 (2), pp. 342-345. |
| DOI: |
10.1364/OL.509772 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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