# Optimal Transport for Super Resolution Applied to Astronomy Imaging

arXiv: https://arxiv.org/abs/2202.05354 · HTML: https://mlsanity.com/paper/2202.05354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [Learning with a Wasserstein Loss](https://mlsanity.com/paper/1506.05439/) (2015)
- [On Lipschitz Bounds of General Convolutional Neural Networks](https://mlsanity.com/paper/1808.01415/) (2018)
- [Imaging with Kantorovich-Rubinstein discrepancy](https://mlsanity.com/paper/1407.0221/) (2014)
- [Convergence Guarantees for Deep Epsilon Greedy Policy Learning](https://mlsanity.com/paper/2112.03376/) (2021)
- [StarcNet: Machine Learning for Star Cluster Identification](https://mlsanity.com/paper/2012.09327/) (2020)
- [Super-resolving star clusters with sheaves](https://mlsanity.com/paper/2106.08123/) (2021)
- [Top-K Ranking Deep Contextual Bandits for Information Selection Systems](https://mlsanity.com/paper/2201.13287/) (2022)

## Abstract (arXiv)

Super resolution is an essential tool in optics, especially on interstellar scales, due to physical laws restricting possible imaging resolution. We propose using optimal transport and entropy for super resolution applications. We prove that the reconstruction is accurate when sparsity is known and noise or distortion is small enough. We prove that the optimizer is stable and robust to noise and perturbations. We compare this method to a state of the art convolutional neural network and get similar results for much less computational cost and greater methodological flexibility.