mlsanityData to 7 Oct 2026

Posterior Continuation with Noise-Conditioned Frequency Exposure for Diffusion Inverse Problems

arXiv 2602.00176 ↗First posted 30 Jan 2026cs.CV cs.AIcited by 0

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Diffusion posterior sampling solves inverse problems by combining a pretrained diffusion prior with measurement-consistency guidance. However, full-band guidance can be unreliable at high noise levels, where clean estimates contain score-induced errors and high-frequency measurement directions are weakly identifiable. We argue that posterior guidance should expose measurement frequencies according to the instantaneous diffusion noise level. Based on this principle, we propose a posterior continuation framework that constructs a family of intermediate posteriors whose likelihood emphasizes currently reliable frequency bands and gradually returns to full-band consistency. We instantiate this framework with a stabilized sampler that combines a diffusion predictor, frequency-limited likelihood refinement, and a Haar-domain commitment rule that commits reliable coarse corrections while deferring weakly identifiable details. Across super-resolution, inpainting, and deblurring, our method achieves competitive-to-state-of-the-art restoration performance, including up to 5 dB PSNR improvement on motion deblurring over strong baselines in evaluations on FFHQ and ImageNet.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 32 of its 42 references that are in the map. First posted on arXiv 30 Jan 2026.

Builds on

32 of 42 references.

Closest ancestors · 12

Same direction, newest first

  1. 2025SILO: Solving Inverse Problems with Latent Operators20 Jan
  2. 2024Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration22 Nov
  3. 2024A Survey on Diffusion Models for Inverse Problems30 Sep
  4. 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing1 Jul
  5. 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction25 Mar
  6. 2024Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors18 Mar
  7. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency16 Jul
  8. 2023Denoising Diffusion Models for Plug-and-Play Image Restoration15 May
  9. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model1 Dec
  10. 2022Parallel Diffusion Models of Operator and Image for Blind Inverse Problems19 Nov
  11. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  12. 2022Denoising Diffusion Restoration Models27 Jan

Other references · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  6. 2022Classifier-Free Diffusion Guidancecited by 5,717
  7. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  8. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  9. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  10. 2023Consistency Modelscited by 1,778
  11. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  12. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
8 more
  1. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 411
  2. 2017Compressed Sensing using Generative Modelscited by 337
  3. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
  4. 2021Deep Equilibrium Architectures for Inverse Problems in Imagingcited by 126
  5. 2024Boosting Diffusion Models with Moving Average Sampling in Frequency Domaincited by 53
  6. 2013Hamiltonian Monte Carlo for Hierarchical Modelscited by 50
  7. 2025A Fourier Space Perspective on Diffusion Modelscited by 48
  8. 2024PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editingcited by 9

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  1. 2024Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
  2. 2026A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
  3. 2025DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
  4. 2025Solving Diffusion Inverse Problems with Restart Posterior Sampling
  5. 2026Provable diffusion-based posterior sampling for linear inverse problems via DDIM
  6. 2026Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models
  7. 2025SURE Guided Posterior Sampling: Trajectory Correction for Diffusion-Based Inverse Problems
  8. 2024Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint