# Image reconstruction through a multimode fiber with a simple neural network architecture

arXiv: https://arxiv.org/abs/2006.05708 · HTML: https://mlsanity.com/paper/2006.05708/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

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

- [Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning](https://mlsanity.com/paper/2301.06496/) (2023)
- [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)

Multimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.