[2605.12573] Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration
[Submitted on 12 May 2026]
Title:Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration
View a PDF of the paper titled Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration, by Davide Evangelista and 3 other authors
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Abstract: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 on this, we propose LAMP, combining the second-order update with the residual correction characterizing a PS technique. LAMP thus inherits a lagged temporal correction, and it can be implemented as a modular plug-in over the PS backbone. We show that LAMP preserves the structure of a posterior sampler, and we perform a one-step risk analysis to characterize when LAMP improves the reverse transition via a bias-variance trade-off. Experiments across multiple imaging tasks demonstrate consistent improvements over strong baselines such as DiffPIR and DDRM, without increasing the number of denoising evaluations.
Comments: 9 Figures, 9 Tables, Submitted to a conference
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.12573 [cs.CV]
(or arXiv:2605.12573v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.12573
arXiv-issued DOI via DataCite
Submission history
From: Davide Evangelista [view email] [v1] Tue, 12 May 2026 11:38:36 UTC (26,302 KB)
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