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.

通过对银河系人口的详细结构进行研究，可以提供有价值的关于其形成和演化机制的深入洞察。本文介绍了一种方法，结合了基于得分的似然特征和扩散模型先验的最新进展，用于进行图像去卷积的贝叶斯分析。该方法在经过最简处理的哈勃太空望远镜（Hubble Space Telescope, HST）数据上应用时，恢复了只能在下一代詹姆斯・韦伯太空望远镜（James Webb Space Telescope, JWST）成像中才能看到的结构。