Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration

• URL: http://arxiv.org/abs/2605.12573v1
• Date: Tue, 12 May 2026 11:38:36 GMT
• Title: Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration
• Abstract summary: Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems.<n>PS relies on instantaneous data-consistent estimates, which induce temporal variability in the reverse dynamics.<n>We propose LAMP, combining the second-order update with the residual correction characterizing a PS technique.
• Score: 1.2891189282516038
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping [2.9985000712079426]
  We replace scalar guidance with a per-noise-level damped Gauss--Newton correction computed in diffusion-state coordinates.<n>It achieves competitive PSNR/SSIM/LPIPS while running markedly faster than most of the compared baselines.
  arXiv  Detail & Related papers  (2026-05-27T05:29:35Z)
• Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation [72.05592785529404]
  We show that the standard plug-in bridge parameterization for UDM is not optimized by the denoising posterior.<n>We also introduce an absorbing-state reformulation of uniform diffusion that preserves the UDM joint law while decomposing it into masked-diffusion-like sampling operations.
  arXiv  Detail & Related papers  (2026-05-21T17:27:19Z)
• ZeroIDIR: Zero-Reference Illumination Degradation Image Restoration with Perturbed Consistency Diffusion Models [72.4683657127516]
  We propose a zero-reference diffusion-based framework, named ZeroIDIR, for illumination image restoration.<n>The framework decouples the restoration process into adaptive illumination correction and diffusion-based reconstruction.<n>Experiments on publicly available benchmarks show that the proposed method outperforms state-of-the-art unsupervised competitors.
  arXiv  Detail & Related papers  (2026-05-12T02:36:49Z)
• TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting [67.91113180885601]
  TimeAPN explicitly models and predicts non-stationary factors from both the time and frequency domains.<n>TimeAPN consistently improves long-term forecasting accuracy across multiple prediction horizons.<n>It outperforms state-of-the-art reversible normalization methods.
  arXiv  Detail & Related papers  (2026-03-18T07:21:24Z)
• Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction [45.25461515976432]
  Plug-and-Play diffusion prior (DP) frameworks have emerged as a powerful paradigm for imaging reconstruction.<n>We present a novel approach to resolving bias-hallucination trade-off, achieving state-of-the-art gradients with significantly accelerated convergence.
  arXiv  Detail & Related papers  (2026-02-26T16:58:43Z)
• Robust Posterior Diffusion-based Sampling via Adaptive Guidance Scale [39.27744518020771]
  We propose an adaptive likelihood step-size strategy to guide the diffusion process for inverse-problem formulations.<n>The resulting approach, Adaptive Posterior diffusion Sampling (AdaPS), is hyper-free and improves reconstruction quality across diverse imaging tasks.
  arXiv  Detail & Related papers  (2025-11-23T14:37:59Z)
• Improving Temporal Consistency and Fidelity at Inference-time in Perceptual Video Restoration by Zero-shot Image-based Diffusion Models [5.61537470581101]
  We address the challenge of improving temporal coherence in video restoration using zero-shot image-based diffusion models.<n>We propose two complementary inference-time strategies: Perceptual Straightening Guidance (PSG) and Ensemble Sampling (MPES)
  arXiv  Detail & Related papers  (2025-10-29T11:40:06Z)
• Solving Inverse Problems with FLAIR [68.87167940623318]
  We present FLAIR, a training-free variational framework that leverages flow-based generative models as prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion [52.315729095824906]
  MLLM Semantic-Corrected Ping-Pong-Ahead Diffusion (PPAD) is a novel framework that introduces a Multimodal Large Language Model (MLLM) as a semantic observer during inference.<n>It performs real-time analysis on intermediate generations, identifies latent semantic inconsistencies, and translates feedback into controllable signals that actively guide the remaining denoising steps.<n>Extensive experiments demonstrate PPAD's significant improvements.
  arXiv  Detail & Related papers  (2025-05-26T14:42:35Z)
• ADT: Tuning Diffusion Models with Adversarial Supervision [16.974169058917443]
  Diffusion models have achieved outstanding image generation by reversing a forward noising process to approximate true data distributions.<n>We propose Adrial Diffusion Tuning (ADT) to stimulate the inference process during optimization and align the final outputs with training data.<n>ADT features a siamese-network discriminator with a fixed pre-trained backbone and lightweight trainable parameters.
  arXiv  Detail & Related papers  (2025-04-15T17:37:50Z)
• On Self-Adaptive Perception Loss Function for Sequential Lossy Compression [29.361832071511795]
  We consider causal, low-latency, sequential lossy compression, with mean squared-error (MSE) as the distortion loss, and a perception loss function (PLF) to enhance the realism of reconstructions.<n>We establish the theoretical rate-distortion-perception function for first-order Markov sources and analyze the Gaussian model in detail.<n>The proposed metric is referred to as self-adaptive perception loss function (PLF-SA), as its behavior adapts to the quality of reconstructed frames.
  arXiv  Detail & Related papers  (2025-02-15T01:41:53Z)
• Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data [54.09959775518994]
  We provide a framework for solving inverse problems with diffusion models learned from linearly corrupted data.<n>We train diffusion models for MRI with access only to subsampled multi-coil measurements at acceleration factors R= 2,4,6,8.<n>For MRI reconstruction in high acceleration regimes, we observe that A-DPS models trained on subsampled data are better suited to solving inverse problems than models trained on fully sampled data.
  arXiv  Detail & Related papers  (2024-03-13T17:28:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.