Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolution, is ill-posed because PSF convolution is not an invertible transformation. Deep generative models are appealing for PSF deconvolution because they can infer a posterior distribution over candidate images that, if convolved with the PSF, could have generated the observation. However, classical deep generative models such as VAEs and GANs often provide inadequate sample diversity. As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images. We demonstrate that this diffusion model captures a greater diversity of possible deconvolutions compared to a conditional VAE.
@article{arxiv.2307.11122,
title = {Diffusion Models for Probabilistic Deconvolution of Galaxy Images},
author = {Zhiwei Xue and Yuhang Li and Yash Patel and Jeffrey Regier},
journal= {arXiv preprint arXiv:2307.11122},
year = {2023}
}
Comments
Accepted to the ICML 2023 Workshop on Machine Learning for Astrophysics