mlsanityData to 14 Oct 2026

Measurement-Aligned Sampling for Inverse Problem

arXiv 2506.11893 ↗First posted 13 Jun 2025cs.LGcited by 1

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

Abstract (arXiv)

Diffusion models provide a powerful way to incorporate complex prior information for solving inverse problems. However, existing methods struggle to correctly incorporate guidance from conflicting signals in the prior and measurement, and often failed to maximizing the consistency to the measurement, especially in the challenging setting of non-Gaussian or unknown noise. To address these issues, we propose Measurement-Aligned Sampling (MAS), a novel framework for linear inverse problem solving that flexibly balances prior and measurement information. MAS unifies and extends existing approaches such as DDNM, TMPD, while generalizing to handle both known Gaussian noise and unknown or non-Gaussian noise types. Extensive experiments demonstrate that MAS consistently outperforms state-of-the-art methods across a variety of tasks, while maintaining relatively low computational cost.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 33 of its 42 references that are in the map. First posted on arXiv 13 Jun 2025.

Builds on

33 of 42 references.

Closest ancestors · 16

Same direction, newest first

  1. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences14 Mar
  2. 2024SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems6 Oct
  3. 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing1 Jul
  4. 2024DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models27 May
  5. 2024Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors18 Mar
  6. 2023Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion1 Dec
  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. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Models7 May
  10. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imaging23 Apr
  11. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model1 Dec
  12. 2022Parallel Diffusion Models of Operator and Image for Blind Inverse Problems19 Nov
4 more
  1. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  2. 2022JPEG Artifact Correction using Denoising Diffusion Restoration Models23 Sep
  3. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraints2 Jun
  4. 2022Denoising Diffusion Restoration Models27 Jan

Other references · 17

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  6. 2022Classifier-Free Diffusion Guidancecited by 5,847
  7. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  8. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  9. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 521
  10. 2023Universal Guidance for Diffusion Modelscited by 437
  11. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Modelcited by 294
  12. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 232
5 more
  1. 2023Manifold Preserving Guided Diffusioncited by 157
  2. 2023Guided Flows for Generative Modeling and Decision Makingcited by 101
  3. 2023Training-free Linear Image Inverses via Flowscited by 72
  4. 2024Guidance with Spherical Gaussian Constraint for Conditional Diffusioncited by 71
  5. 2024Particle Denoising Diffusion Samplercited by 65

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Livin' on a Prior: Likelihood Score Approximation for Inverse Problemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Solving Diffusion Inverse Problems with Restart Posterior Sampling
  2. 2025Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models
  3. 2024Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization
  4. 2024Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint
  5. 2023Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling
  6. 2025MACS: Measurement-Aware Consistency Sampling for Inverse Problems
  7. 2026SNaP: One-Step Posterior Sampling for Noisy Inverse Problems
  8. 2024Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements