Learning the Night Sky with Deep Generative Priors
Abstract
Recovering sharper images from blurred observations, referred to as deconvolution, is an ill-posed problem where classical approaches often produce unsatisfactory results. In ground-based astronomy, combining multiple exposures to achieve images with higher signal-to-noise ratios is complicated by the variation of point-spread functions across exposures due to atmospheric effects. We develop an unsupervised multi-frame method for denoising, deblurring, and coadding images inspired by deep generative priors. We use a carefully chosen convolutional neural network architecture that combines information from multiple observations, regularizes the joint likelihood over these observations, and allows us to impose desired constraints, such as non-negativity of pixel values in the sharp, restored image. With an eye towards the Rubin Observatory, we analyze 4K by 4K Hyper Suprime-Cam exposures and obtain preliminary results which yield promising restored images and extracted source lists.
Cite
@article{arxiv.2302.02030,
title = {Learning the Night Sky with Deep Generative Priors},
author = {Fausto Navarro and Daniel Hall and Tamas Budavari and Yashil Sukurdeep},
journal= {arXiv preprint arXiv:2302.02030},
year = {2025}
}