Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such as ADMM remains nontrivial. Two central challenges arise: i) the mismatch between the noisy data manifolds used to train the score functions and the geometry of ADMM iterates, especially due to the influence of dual variables, and ii) the lack of convergence understanding when ADMM is equipped with score-based denoisers. To address the manifold mismatch issue, we propose ADMM plug-and-play (ADMM-PnP) with the AC-DC denoiser, a new framework that embeds a three-stage denoiser into ADMM: (1) auto-correction (AC) via additive Gaussian n...
| Authors | Rajesh Shrestha, Xiao Fu |
|---|---|
| Primary category | cs.LG |
| Published | 10 March 2026 |
| Last updated at source | 11 May 2026 |
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