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Diffusion priors for denoising and deblurring

Use diffusion models as priors for blind denoising and real-world deblurring.

41 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Image restoration, keyword cluster “image denoising · blur · noise”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

14 papers in 2023–24 and 17 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 4.5% to 3.9%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 2162017: 0172018: 0182019: 2192020: 3202021: 3212022: 0222023: 7232024: 7242025: 7252026: 1026

Most cited paper of each year

Earliest papers here

What has been tried

31 papers here report 342 measured changes (a component added, removed or swapped, compared with a baseline): helped 71%, no clear effect 8%, hurt 17%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Add matched Markov-chain start-state selectionKAIST · inference time311s -> 41sHelpedUnmixing Diffusion for Self-Supervised Hyperspectral Image Denoising (CVPR 2024 · 38 citations)
remove class-conditioningImageNet · linear probe accuracy57.5% to 62.5%HelpedDeconstructing Denoising Diffusion Models for Self-Supervised Learning (ICLR 2025 · 17 citations)
Add the autoencoding regularization term (λ=1) rather than disable it (λ=0)MRI 4x and CT 18 views · PSNR (dB)HelpedImage Reconstruction Via Autoencoding Sequential Deep Image Prior (NeurIPS 2024 · 9 citations)
add the hyper-network NM and customized parametersSynData simple meshes · Ea4.91 -> 4.32HelpedHyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics (CVPR 2024 · 6 citations)
Add LKPN-guided EAC structural conditioning to ControlNetReal Images · MANIQA0.5544 -> 0.5963HelpedDeblurDiff: Real-Word Image Deblurring with Generative Diffusion Models (NeurIPS 2025 · 5 citations)
Add RG-Diff and SAM prompt fusionSIDD · PSNR51.32 -> 51.55HelpedPrompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single Image Denoising (AAAI 2025 · 4 citations)
Add adaptive likelihood estimation (ALE)SIDD Validation · PSNR/SSIM32.12/0.741 -> 34.63/0.870HelpedDiffusion Priors for Variational Likelihood Estimation and Image Denoising (NeurIPS 2024 · 4 citations)
Use an anechoic clean-speech prior instead of a reverberant-speech priorSMS-WSJ 3-channel · SDR (dB)15.1±1.1 -> 15.8±1.2HelpedArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior (ICML 2025 · 0 citations)
Use disentangled decoders rather than a shared single decoder during trainingSuper resolution · PSNRapproximately over a 1 dB to 1.5 dB improvementHelpedUGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights (NeurIPS 2025 · 0 citations)
Add detail refinementM4Raw test, T1 · PSNR32.59 -> 32.77HelpedScore-based Self-supervised MRI Denoising (ICLR 2025 · 0 citations)
remove the Fusion processIn-vivo dMRI denoising · image driftHurtSelf-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement (ICLR 2025 · 0 citations)
Increase the Gaussian-noise lubrication coefficient γNatural-image interpolation · image artifactsHelpedNoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation (ICLR 2024 · 0 citations)

What reviewers will expect

Datasets and baselines named in the 9 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • ImageNet · 2 of 9 papers
  • fastMRI · 2 of 9 papers

Baselines

  • Noisier2Noise · 2 of 9 papers
  • Recorrupted2Recorrupted · 2 of 9 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 31 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 35 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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