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Image Restoration using Total Variation Regularized Deep Image Prior

arXiv 1810.12864 ↗First posted 30 Oct 2018cs.CVcited by 65

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Abstract (arXiv)

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring.

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2018.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 16; 2024: 16; 2025: 8; 2026: 7.

65 papers.

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  1. 2021Explore Image Deblurring via Blur Kernel Spacecited by 58
  2. 2021Early Stopping for Deep Image Priorcited by 44
  3. 2021On Measuring and Controlling the Spectral Bias of the Deep Image Priorcited by 36
  4. 2024Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problemscited by 21
  5. 2022Dressing in the Wild by Watching Dance Videoscited by 20
  6. 2023NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matchingcited by 18
  7. 2024Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problemscited by 18
  8. 2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
  9. 2021Untrained networks for compressive lensless photographycited by 13
  10. 2023Regularize implicit neural representation by itselfcited by 13
  11. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstructioncited by 13
  12. 2021Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priorscited by 12
38 more
  1. 2023The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Priorcited by 11
  2. 2024Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstructioncited by 11
  3. 2022Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Priorcited by 10
  4. 2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
  5. 2023Deep Internal Learning: Deep Learning from a Single Inputcited by 9
  6. 2021Image Restoration by Deep Projected GSUREcited by 8
  7. 2024Patch-Based Diffusion Models Beat Whole-Image Models for Mismatched Distribution Inverse Problemscited by 8
  8. 2022Computing Multiple Image Reconstructions with a Single Hypernetworkcited by 7
  9. 2022PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descentcited by 7
  10. 2024NeurTV: Total Variation on the Neural Domaincited by 7
  11. 2023SEMPART: Self-supervised Multi-resolution Partitioning of Image Semanticscited by 4
  12. 2023Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imagingcited by 4
  13. 2022Priors in Deep Image Restoration and Enhancement: A Surveycited by 3
  14. 2022Reversing Image Signal Processors by Reverse Style Transferringcited by 3
  15. 2023Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problemscited by 3
  16. 2023Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularizationcited by 3
  17. 2023Convergence Guarantees of Overparametrized Wide Deep Inverse Priorcited by 2
  18. 2023SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstructioncited by 2
  19. 2023Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arccited by 2
  20. 2024Boosting of Implicit Neural Representation-based Image Denoisercited by 2
  21. 2024Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initializationcited by 2
  22. 2024DeepTV: A neural network approach for total variation minimizationcited by 2
  23. 2025Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoiserscited by 2
  24. 2022MetaDIP: Accelerating Deep Image Prior with Meta Learningcited by 1
  25. 2022Residual Back Projection With Untrained Neural Networkscited by 1
  26. 2023Image Reconstruction via Deep Image Prior Subspacescited by 1
  27. 2023Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Priorcited by 1
  28. 2023Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerationscited by 1
  29. 2023VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variationcited by 1
  30. 2023GC-VTON: Predicting Globally Consistent and Occlusion Aware Local Flows with Neighborhood Integrity Preservation for Virtual Try-oncited by 1
  31. 2024Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overviewcited by 1
  32. 2024On normalization-equivariance properties of supervised and unsupervised denoising methods: a surveycited by 1
  33. 2024SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processingcited by 1
  34. 2025Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Modelscited by 1
  35. 2025Deep Spectral Priorcited by 1
  36. 2021Deep Chaos Synchronizationcited by 0
  37. 2021Using Deep Image Prior to Assist Variational Selective Segmentation Deep Learning Algorithmscited by 0
  38. 2024Fast Unsupervised Tensor Restoration via Low-rank Deconvolutioncited by 0

50 most cited of 65 All 65 (JSON)