mlsanityData to 7 Oct 2026

MACS: Measurement-Aware Consistency Sampling for Inverse Problems

arXiv 2510.02208 ↗First posted 2 Oct 2025eess.IV cs.CV cs.LGcited by 0

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

Abstract (arXiv)

Diffusion models have emerged as powerful generative priors for solving inverse imaging problems. However, their practical deployment is hindered by the substantial computational cost of slow, multi-step sampling. Although Consistency Models (CMs) address this limitation by enabling high-quality generation in only one or a few steps, their direct application to inverse problems has remained largely unexplored. This paper introduces a modified consistency sampling framework specifically designed for inverse problems. The proposed approach regulates the sampler's stochasticity through a measurement-consistency mechanism that leverages the degradation operator, thereby enforcing fidelity to the observed data while preserving the computational efficiency of consistency-based generation. Comprehensive experiments on the Fashion-MNIST and LSUN Bedroom datasets demonstrate consistent improvements across both perceptual and pixel-level metrics, including the Fr'echet Inception Distance (FID), Kernel Inception Distance (KID), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), compared with baseline consistency and diffusion-based sampling methods. The proposed method achieves competitive or superior reconstruction quality with only a small number of sampling steps.

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 50 of its 62 references that are in the map. First posted on arXiv 2 Oct 2025.

Builds on

50 of 62 references.

Closest ancestors · 17

Same direction, newest first

  1. 2025InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems2 Apr
  2. 2025LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization16 Mar
  3. 2025SILO: Solving Inverse Problems with Latent Operators20 Jan
  4. 2024Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)29 Dec
  5. 2024VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models29 Nov
  6. 2024Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models21 Oct
  7. 2024A Survey on Diffusion Models for Inverse Problems30 Sep
  8. 2024Solving Video Inverse Problems Using Image Diffusion Models4 Sep
  9. 2024CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems17 Jul
  10. 2024DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models1 Jul
  11. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency16 Jul
  12. 2023Direct Diffusion Bridge using Data Consistency for Inverse Problems31 May
5 more
  1. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems10 Mar
  2. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model1 Dec
  3. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  4. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraints2 Jun
  5. 2022Denoising Diffusion Restoration Models27 Jan

Other references · 33

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  8. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  9. 2022Video Diffusion Modelscited by 2,280
  10. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  11. 2023Consistency Modelscited by 1,778
  12. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
21 more
  1. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  2. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  3. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  4. 2023Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inferencecited by 826
  5. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  6. 2017The Perception-Distortion Tradeoffcited by 582
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  8. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 361
  9. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  10. 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 194
  11. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  12. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  13. 2024Consistency Models Made Easycited by 140
  14. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Modelscited by 130
  15. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  16. 2024Multistep Consistency Modelscited by 67
  17. 2022Solving Audio Inverse Problems with a Diffusion Modelcited by 62
  18. 2021A Theory of the Distortion-Perception Tradeoff in Wasserstein Spacecited by 52
  19. 2024DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformcited by 44
  20. 2021High Perceptual Quality Image Denoising with a Posterior Sampling CGANcited by 33
  21. 2024Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Modelscited by 12

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  1. 2024Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint
  2. 2024SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems
  3. 2024Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
  4. 2025Measurement-Aligned Sampling for Inverse Problem
  5. 2026Posterior Continuation with Noise-Conditioned Frequency Exposure for Diffusion Inverse Problems
  6. 2026Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates
  7. 2024Deep Data Consistency: a Fast and Robust Diffusion Model-based Solver for Inverse Problems
  8. 2025Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection