mlsanityData to 14 Oct 2026

DeepRED: Deep Image Prior Powered by RED

arXiv 1903.10176 ↗First posted 25 Mar 2019cs.CV eess.IVcited by 85

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Abstract (arXiv)

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.

Key facts. Cited by 85 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Mar 2019.

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Citing papers per year: 2021: 21; 2022: 11; 2023: 22; 2024: 15; 2025: 9; 2026: 7.

85 papers.

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  1. 2022Denoising Diffusion Restoration Modelscited by 963
  2. 2021Learning to Optimize: A Primer and A Benchmarkcited by 208
  3. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Modelscited by 157
  4. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  5. 2021Early Stopping for Deep Image Priorcited by 44
  6. 2021Rethinking Deep Image Prior for Denoisingcited by 38
  7. 2021On Measuring and Controlling the Spectral Bias of the Deep Image Priorcited by 36
  8. 2021Self-supervised Neural Networks for Spectral Snapshot Compressive Imagingcited by 35
  9. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  10. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  11. 2022ADIR: Adaptive Diffusion for Image Reconstructioncited by 20
  12. 2021Fixed-Point and Objective Convergence of Plug-and-Play Algorithmscited by 19
38 more
  1. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  2. 2023NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matchingcited by 18
  3. 2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
  4. 2021Untrained Graph Neural Networks for Denoisingcited by 15
  5. 2023Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurringcited by 14
  6. 2021Untrained networks for compressive lensless photographycited by 13
  7. 2023Regularize implicit neural representation by itselfcited by 13
  8. 2021ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Priorcited by 12
  9. 2023The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Priorcited by 11
  10. 2024Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstructioncited by 11
  11. 2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
  12. 2023Deep Internal Learning: Deep Learning from a Single Inputcited by 9
  13. 2021Image Restoration by Deep Projected GSUREcited by 8
  14. 2022Deep Model-Based Architectures for Inverse Problems under Mismatched Priorscited by 8
  15. 2023Revisiting Implicit Neural Representations in Low-Level Visioncited by 8
  16. 2024Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priorscited by 8
  17. 2025A Baseline Method for Removing Invisible Image Watermarks using Deep Image Priorcited by 7
  18. 2026FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearningcited by 6
  19. 2022Blind Image Deconvolution Using Variational Deep Image Priorcited by 5
  20. 2023Recurrent Spike-based Image Restoration under General Illuminationcited by 5
  21. 2024Equivariant Imaging for Self-supervised Hyperspectral Image Inpaintingcited by 5
  22. 2021Patch-Based Image Restoration using Expectation Propagationcited by 4
  23. 2023Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imagingcited by 4
  24. 2024Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noisecited by 4
  25. 2025Multilook Coherent Imaging: Theoretical Guarantees and Algorithmscited by 4
  26. 2022Monotonically Convergent Regularization by Denoisingcited by 3
  27. 2022Priors in Deep Image Restoration and Enhancement: A Surveycited by 3
  28. 2022PIP: Positional-encoding Image Priorcited by 3
  29. 2023Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problemscited by 3
  30. 2024One-Shot Image Restorationcited by 3
  31. 2021Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learningcited by 2
  32. 2021Wiener Guided DIP for Unsupervised Blind Image Deconvolutioncited by 2
  33. 2022Laplacian Pyramid-like Autoencodercited by 2
  34. 2022Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation timecited by 2
  35. 2023RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imagingcited by 2
  36. 2023An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removalcited by 2
  37. 2023Convergence Guarantees of Overparametrized Wide Deep Inverse Priorcited by 2
  38. 2023RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imagingcited by 2

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