mlsanityData to 14 Oct 2026

Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems

arXiv 2407.10641 ↗First posted 15 Jul 2024cs.CV cs.LGcited by 21

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

Abstract (arXiv)

Recent inverse problem solvers that leverage generative diffusion priors have garnered significant attention due to their exceptional quality. However, adaptation of the prior is necessary when there exists a discrepancy between the training and testing distributions. In this work, we propose deep diffusion image prior (DDIP), which generalizes the recent adaptation method of SCD by introducing a formal connection to the deep image prior. Under this framework, we propose an efficient adaptation method dubbed D3IP, specified for 3D measurements, which accelerates DDIP by orders of magnitude while achieving superior performance. D3IP enables seamless integration of 3D inverse solvers and thus leads to coherent 3D reconstruction. Moreover, we show that meta-learning techniques can also be applied to yield even better performance. We show that our method is capable of solving diverse 3D reconstructive tasks from the generative prior trained only with phantom images that are vastly different from the training set, opening up new opportunities of applying diffusion inverse solvers even when training with gold standard data is impossible. Code: https://github.com/HJ-harry/DDIP3D

Key facts. Cited by 21 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 46 of its 66 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

46 of 66 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023Denoising Diffusion Models for Plug-and-Play Image Restoration15 May
  2. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imaging23 Apr
  3. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems10 Mar
  4. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model1 Dec
  5. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  6. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraints2 Jun
  7. 2022Denoising Diffusion Restoration Models27 Jan

Other references · 39

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  7. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  8. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  9. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  10. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  11. 2013Auto-Encoding Variational Bayescited by 4,053
  12. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
27 more
  1. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  2. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  3. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,219
  4. 2017Deep Image Priorcited by 1,098
  5. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  6. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 375
  7. 2021Score-based diffusion models for accelerated MRIcited by 311
  8. 2023Common Diffusion Noise Schedules and Sample Steps are Flawedcited by 310
  9. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  10. 2023Preserve Your Own Correlation: A Noise Prior for Video Diffusion Modelscited by 285
  11. 2017Learned Primal-dual Reconstructioncited by 251
  12. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Modelscited by 132
  13. 2018Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networkscited by 120
  14. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 111
  15. 2021Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimationcited by 97
  16. 2023FreeInit: Bridging Initialization Gap in Video Diffusion Modelscited by 95
  17. 2019Time-Dependent Deep Image Prior for Dynamic MRIcited by 70
  18. 2018Image Restoration using Total Variation Regularized Deep Image Priorcited by 65
  19. 2020Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methodscited by 56
  20. 2023Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Modelscited by 53
  21. 2019Neural reparameterization improves structural optimizationcited by 44
  22. 2023GSURE-Based Diffusion Model Training with Corrupted Datacited by 40
  23. 2021Rethinking Deep Image Prior for Denoisingcited by 38
  24. 2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 20
  25. 2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
  26. 2023Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Modelscited by 8
  27. 2020Fast Variable Density Poisson-Disc Sample Generation with Directional Variationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 11; 2026: 7.

21 papers.

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By area:

  1. 2024A Survey on Diffusion Models for Inverse Problemscited by 180
  2. 2025Diffusion models for inverse problemscited by 16
  3. 2025JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformerscited by 11
  4. 2024Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problemscited by 8
  5. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  6. 2025PET Image Reconstruction Using Deep Diffusion Image Priorcited by 6
  7. 2024Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representationcited by 4
  8. 2025Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Modelscited by 4
  9. 2026Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticitycited by 3
  10. 2025Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructionscited by 2
  11. 2026Cross-Modal Guidance for Fast Diffusion-Based Computed Tomographycited by 2
  12. 2025Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CTcited by 1
9 more
  1. 2025Equivariant Sampling for Improving Diffusion Model-based Image Restorationcited by 1
  2. 2025PMA-Diffusion: A Physics-guided Mask-Aware Diffusion Framework for TSE from Sparse Observationscited by 1
  3. 2025Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstructioncited by 1
  4. 2026Regularizing INR with diffusion prior self-supervised 3D reconstruction of neutron computed tomography datacited by 1
  5. 2025Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusioncited by 0
  6. 2026Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuningcited by 0
  7. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Modelcited by 0
  8. 2026Backward SDEs-based Diffusion for Physics-Constrained Generationcited by 0
  9. 2026STAR: Structure-aware Test-time Adaptation for diffusion-based light field Reconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Deep Data Consistency: a Fast and Robust Diffusion Model-based Solver for Inverse Problems
  2. 2025DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
  3. 2024Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
  4. 2025SAIP: A Plug-and-Play Scale-adaptive Module in Diffusion-based Inverse Problems
  5. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
  6. 2024Enhancing and Accelerating Diffusion-Based Inverse Problem Solving through Measurements Optimization
  7. 2024Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
  8. 2024G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving