Diffusion Models for Probabilistic Deconvolution of Galaxy Images
This work addresses image quality issues for astronomers, but it is incremental as it applies an existing diffusion model to a specific domain.
The authors tackled the ill-posed problem of PSF deconvolution in galaxy images by proposing a classifier-free conditional diffusion model, which captures greater diversity of possible deconvolutions compared to a conditional VAE.
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.