Diffusion Models for Probabilistic Deconvolution of Galaxy Images
Zhiwei Xue Yuhang Li Yash Patel Jeffrey Regier
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.