# Noise-resilient approach for deep tomographic imaging

arXiv: https://arxiv.org/abs/2211.15456 · HTML: https://mlsanity.com/paper/2211.15456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning](https://mlsanity.com/paper/2007.10734/) (2020)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)

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

- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2307.04771/) (2023)

## Abstract (arXiv)

We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.