mlsanityData to 7 Oct 2026

Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques

arXiv 2507.08375 ↗First posted 11 Jul 2025cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the performance of a wide range of downstream computer vision tasks. This survey presents a comprehensive review of video restoration and enhancement techniques with a particular focus on unsupervised approaches. We begin by outlining the most common video degradations and their underlying causes, followed by a review of early conventional and deep learning methods-based, highlighting their strengths and limitations. We then present an in-depth overview of unsupervised methods, categorise by their fundamental approaches, including domain translation, self-supervision signal design and blind spot or noise-based methods. We also provide a categorization of loss functions employed in unsupervised video restoration and enhancement, and discuss the role of paired synthetic datasets in enabling objective evaluation. Finally, we identify key challenges and outline promising directions for future research in this field.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 102 of its 214 references that are in the map. First posted on arXiv 11 Jul 2025.

Builds on

102 of 214 references.

References · 102

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  5. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  6. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  7. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
  8. 2017Deep Image Priorcited by 1,097
  9. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 974
  10. 2022Denoising Diffusion Restoration Modelscited by 951
  11. 2022Simple Baselines for Image Restorationcited by 701
  12. 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 541
38 more
  1. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  2. 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 492
  3. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  4. 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
  5. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 437
  6. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 340
  7. 2018Scale-recurrent Network for Deep Image Deblurringcited by 332
  8. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  9. 2020Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancementcited by 303
  10. 2020Detection and Tracking Meet Drones Challengecited by 261
  11. 2022Vision Transformers for Single Image Dehazingcited by 255
  12. 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 250
  13. 2018Learning Blind Video Temporal Consistencycited by 247
  14. 2020Multi-Scale Boosted Dehazing Network with Dense Feature Fusioncited by 229
  15. 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
  16. 2022VRT: A Video Restoration Transformercited by 219
  17. 2020CycleISP: Real Image Restoration via Improved Data Synthesiscited by 212
  18. 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
  19. 2021Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Imagescited by 171
  20. 2022Recurrent Video Restoration Transformer with Guided Deformable Attentioncited by 168
  21. 2019FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimationcited by 128
  22. 2017WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Imagescited by 112
  23. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  24. 2015UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Trackingcited by 98
  25. 2022Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imagingcited by 94
  26. 2019Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statisticscited by 92
  27. 2023Implicit Neural Representation for Cooperative Low-light Image Enhancementcited by 92
  28. 2020Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenescited by 77
  29. 2021Physics-based Noise Modeling for Extreme Low-light Photographycited by 67
  30. 2019Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Datasetcited by 65
  31. 2020Artificial Intelligence in the Creative Industries: A Reviewcited by 61
  32. 2021Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancementcited by 60
  33. 2021Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transformcited by 50
  34. 2019Video Object Segmentation and Tracking: A Surveycited by 49
  35. 2022Dancing under the stars: video denoising in starlightcited by 47
  36. 2020Unsupervised Deep Video Denoisingcited by 44
  37. 2021Plug-and-Play Algorithms for Video Snapshot Compressive Imagingcited by 42
  38. 2018Reblur2Deblur: Deblurring Videos via Self-Supervised Learningcited by 38

50 most cited of 102 All 102 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstructioncited by 0