Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration
Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Yet, its standard formulations rely on instantaneous data-consistent estimates, which induce temporal variability in the reverse dynamics. We reinterpret PS from a dynamical perspective, showing that the standard PS update corresponds to a first-order discretization of the diffusion dynamics plus a residual correction capturing the mismatch between the denoised prediction and the data-consistent estimate. A second-order discretization, however, naturally introduces a temporal correction based on the variation of consecutive estimates. Building...
| Authors | Davide Evangelista, Elena Morotti, Francesco Pivi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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