# AstroClearNet: Deep image prior for multi-frame astronomical image restoration

arXiv: https://arxiv.org/abs/2504.06463 · HTML: https://mlsanity.com/paper/2504.06463/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Neural Blind Deconvolution Using Deep Priors](https://mlsanity.com/paper/1908.02197/) (2019)
- [Blind Image Deconvolution using Deep Generative Priors](https://mlsanity.com/paper/1802.04073/) (2018)
- [Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems](https://mlsanity.com/paper/1912.03980/) (2019)
- [Robust Statistics for Image Deconvolution](https://mlsanity.com/paper/1711.02793/) (2017)
- [Learning the Night Sky with Deep Generative Priors](https://mlsanity.com/paper/2302.02030/) (2023)

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

- [AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data](https://mlsanity.com/paper/2604.16793/) (2026)

## Abstract (arXiv)

Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance signal-to-noise ratios is further complicated by variations in the point-spread function caused by atmospheric turbulence. In this work, we present a self-supervised multi-frame method, based on deep image priors, for denoising, deblurring, and coadding ground-based exposures. Central to our approach is a carefully designed convolutional neural network that integrates information across multiple observations and enforces physically motivated constraints. We demonstrate the method's potential by processing Hyper Suprime-Cam exposures, yielding promising preliminary results with sharper restored images.