  Examining the detailed structure of galaxy populations provides valuable
insights into their formation and evolution mechanisms. Significant barriers to
such analysis are the non-trivial noise properties of real astronomical images
and the point spread function (PSF) which blurs structure. Here we present a
framework which combines recent advances in score-based likelihood
characterization and diffusion model priors to perform a Bayesian analysis of
image deconvolution. The method, when applied to minimally processed
\emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have
otherwise only become visible in next-generation \emph{James Webb Space
Telescope} (\emph{JWST}) imaging.
