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

arXiv: https://arxiv.org/abs/2312.07102 · HTML: https://mlsanity.com/paper/2312.07102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Quantitative Phase Imaging and Artificial Intelligence: A Review](https://mlsanity.com/paper/1806.03982/) (2018)
- [Quantitative phase imaging through an ultra-thin lensless fiber endoscope](https://mlsanity.com/paper/2112.12055/) (2021)

## Built on by (most cited first, 1 of 1)

- [Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology](https://mlsanity.com/paper/2407.18456/) (2024)

## Abstract (arXiv)

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.