# Lunar surface image restoration using U-net based deep neural networks

arXiv: https://arxiv.org/abs/1904.06683 · HTML: https://mlsanity.com/paper/1904.06683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2019.

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

- [Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects](https://mlsanity.com/paper/2501.13009/) (2025)

## Abstract (arXiv)

Image restoration is a technique that reconstructs a feasible estimate of the original image from the noisy observation. In this paper, we present a U-Net based deep neural network model to restore the missing pixels on the lunar surface image in a context-aware fashion, which is often known as image inpainting problem. We use the grayscale image of the lunar surface captured by Multiband Imager (MI) onboard Kaguya satellite for our experiments and the results show that our method can reconstruct the lunar surface image with good visual quality and improved PSNR values.