mlsanityData to 14 Oct 2026

Dual Ascent Diffusion for Inverse Problems

arXiv 2505.17353 ↗First posted 23 May 2025cs.CV cs.AI cs.LGcited by 3

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

Abstract (arXiv)

Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a powerful prior for solving these problems. Existing maximum-a-posteriori (MAP) or posterior sampling approaches, however, rely on different computational approximations, leading to inaccurate or suboptimal samples. To address this issue, we introduce a new approach to solving MAP problems with diffusion model priors using a dual ascent optimization framework. Our framework achieves better image quality as measured by various metrics for image restoration problems, it is more robust to high levels of measurement noise, it is faster, and it estimates solutions that represent the observations more faithfully than the state of the art.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 31 of its 45 references that are in the map. First posted on arXiv 23 May 2025.

Builds on

31 of 45 references.

Closest ancestors · 13

Same direction, newest first

  1. 2024A Survey on Diffusion Models for Inverse Problems30 Sep
  2. 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing1 Jul
  3. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors29 May
  4. 2024DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models27 May
  5. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency16 Jul
  6. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models2 Jul
  7. 2023Denoising Diffusion Models for Plug-and-Play Image Restoration15 May
  8. 2023A Variational Perspective on Solving Inverse Problems with Diffusion Models7 May
  9. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imaging23 Apr
  10. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model1 Dec
  11. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  12. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraints2 Jun
1 more
  1. 2022Denoising Diffusion Restoration Models27 Jan

Other references · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  8. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  9. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  10. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 672
  11. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 635
  12. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 521
6 more
  1. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  2. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 246
  3. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  4. 2021Explore Image Deblurring via Blur Kernel Spacecited by 58
  5. 2024Solving Inverse Problems in Protein Space Using Diffusion-Based Priorscited by 13
  6. 2024Generative modeling of protein ensembles guided by crystallographic electron densitiescited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstructioncited by 0
  2. 2026CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallographycited by 0
  3. 2026Feasible Flow Matching for Graph Reconstruction via Within-Sampling Primal-Dual Guidancecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Diffusion models for inverse problems
  2. 2026Optimizing Diffusion Priors in Image Reconstruction from a Single Observation
  3. 2024Inverse Problems with Diffusion Models: A MAP Estimation Perspective
  4. 2024Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
  5. 2025Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
  6. 2026Principled Design of Diffusion-based Optimizers for Inverse Problems
  7. 2025Solving Diffusion Inverse Problems with Restart Posterior Sampling
  8. 2024Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements