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

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.

@article{sukurdeep2025_2504.06463,
  title={ AstroClearNet: Deep image prior for multi-frame astronomical image restoration },
  author={ Yashil Sukurdeep and Fausto Navarro and Tamás Budavári },
  journal={arXiv preprint arXiv:2504.06463},
  year={ 2025 }
}