mlsanityData to 7 Oct 2026

Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction

arXiv 2510.03352 ↗First posted 2 Oct 2025cs.CV cs.AI cs.LGcited by 1

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

Abstract (arXiv)

Diffusion models have been used as priors for solving inverse problems. However, existing approaches typically overlook side information that could significantly improve reconstruction quality, especially in severely ill-posed settings. In this work, we propose a novel framework that incorporates side information into existing diffusion-based inverse problem solvers via inference-time search, in a plug-and-play, training-free manner. Through extensive experiments across a range of inverse problems, including inpainting, super-resolution, and several deblurring tasks, and across multiple diffusion-based inverse problem solvers (DPS, DAPS, and MPGD), we show that augmenting each solver with our framework consistently improves the quality of the reconstructions over the corresponding original method. To demonstrate the generality of our approach, we consider diverse forms of side information, including reference images, textual descriptions, and anatomical MRI scans. The code is available at this hrefhttps://github.com/mahdi-farahbakhsh/DISSrepositoryfootnotehttps://github.com/mahdi-farahbakhsh/DISS.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 54 of its 72 references that are in the map. First posted on arXiv 2 Oct 2025.

Builds on

54 of 72 references.

Closest ancestors · 13

Same direction, newest first

  1. 2024Leveraging Multimodal Diffusion Models to Accelerate Imaging with Side Information7 Oct
  2. 2024A Survey on Diffusion Models for Inverse Problems30 Sep
  3. 2024Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing1 Jul
  4. 2024Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors18 Mar
  5. 2023Regularization by Texts for Latent Diffusion Inverse Solvers27 Nov
  6. 2023Prompt-tuning latent diffusion models for inverse problems2 Oct
  7. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency16 Jul
  8. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models2 Jul
  9. 2023Generative Diffusion Prior for Unified Image Restoration and Enhancement3 Apr
  10. 2022Parallel Diffusion Models of Operator and Image for Blind Inverse Problems19 Nov
  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 · 41

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2020Denoising Diffusion Implicit Modelscited by 9,655
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  9. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  10. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  11. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  12. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
29 more
  1. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  2. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  3. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  4. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  5. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,010
  6. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
  7. 2024Simple and Effective Masked Diffusion Language Modelscited by 800
  8. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  9. 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 444
  10. 2023Universal Guidance for Diffusion Modelscited by 426
  11. 2022AdaFace: Quality Adaptive Margin for Face Recognitioncited by 390
  12. 2023FreeDoM: Training-Free Energy-Guided Conditional Diffusion Modelcited by 290
  13. 2024Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoningcited by 267
  14. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
  15. 2025A General Framework for Inference-time Scaling and Steering of Diffusion Modelscited by 201
  16. 2023Manifold Preserving Guided Diffusioncited by 153
  17. 2025Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scalingcited by 135
  18. 2022Protein structure generation via folding diffusioncited by 113
  19. 2024TFG: Unified Training-Free Guidance for Diffusion Modelscited by 103
  20. 2025Test-time Alignment of Diffusion Models without Reward Over-optimizationcited by 89
  21. 2025Dynamic Search for Inference-Time Alignment in Diffusion Modelscited by 36
  22. 2025Variational Control for Guidance in Diffusion Modelscited by 22
  23. 2022Unrestricted Black-box Adversarial Attack Using GAN with Limited Queriescited by 18
  24. 2023MRI Reconstruction with Side Information using Diffusion Modelscited by 15
  25. 2025ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioningcited by 8
  26. 2025A Trust-Guided Approach to MR Image Reconstruction with Side Informationcited by 6
  27. 2025Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?cited by 6
  28. 2013Simple Bounds for Noisy Linear Inverse Problems with Exact Side Informationcited by 2
  29. 2019Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Informationcited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Iterative Inference-time Scaling with Adaptive Frequency Steering for Image Super-Resolutioncited by 0

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