# Image transmission through a flexible multimode fiber by deep learning

arXiv: https://arxiv.org/abs/2011.05144 · HTML: https://mlsanity.com/paper/2011.05144/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Self-attention-based non-linear basis transformations for compact latent space modelling of dynamic optical fibre transmission matrices](https://mlsanity.com/paper/2406.07775/) (2024)
- [Mismatch reconstruction theory for unknown measurement matrix in imaging through multimode fiber bending](https://mlsanity.com/paper/2510.21787/) (2025)
- [HistoSpeckle-Net: Mutual Information-Guided Deep Learning for high-fidelity reconstruction of complex OrganAMNIST images via perturbed Multimode Fibers](https://mlsanity.com/paper/2511.20245/) (2025)

## Abstract (arXiv)

When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber-based telecommunication and endoscopy. To overcome this challenge, a deep learning approach that generalizes over mechanical perturbations is presented. Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 meter-long randomly perturbed multimode fiber is demonstrated. The model's success is explained by hidden correlations in the speckle of random fiber conformations.