Dual Ascent Diffusion for Inverse Problems
Journal:
arXiv
Published Date:
May 23, 2025
Abstract
Ill-posed inverse problems are fundamental in many domains, ranging from
astrophysics to medical imaging. Emerging diffusion models provide a powerful
prior for solving these problems. Existing maximum-a-posteriori (MAP) or
posterior sampling approaches, however, rely on different computational
approximations, leading to inaccurate or suboptimal samples. To address this
issue, we introduce a new approach to solving MAP problems with diffusion model
priors using a dual ascent optimization framework. Our framework achieves
better image quality as measured by various metrics for image restoration
problems, it is more robust to high levels of measurement noise, it is faster,
and it estimates solutions that represent the observations more faithfully than
the state of the art.
