Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs as powerful regularizers, but they are susceptible to local minima and noise overfitting. Although DMs can provide strong priors for Bayesian approaches, enforcing measurement consistency during the denoising process leads to manifold infeasibility issues. We propose Noise-space Hamiltonian Monte Carlo (N-HMC), a posterior sampling method that treats reverse diffusion as a deterministic mapping from initial noise to clean images. N-HMC enables comprehensive exploration of the solution space, avoiding local optima. By moving inference entirely...
| Authors | Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen et al. |
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| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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