mlsanityData to 7 Oct 2026

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

arXiv 1612.02177 ↗First posted 7 Dec 2016cs.CVcited by 722

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Cited by 84 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation. To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear. Moreover, recent machine learning based methods also depend on synthetic blur datasets generated under these assumptions. This makes conventional deblurring methods fail to remove blurs where blur kernel is difficult to approximate or parameterize (e.g. object motion boundaries). In this work, we propose a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources. Together, we present multi-scale loss function that mimics conventional coarse-to-fine approaches. Furthermore, we propose a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera. With the proposed model trained on this dataset, we demonstrate empirically that our method achieves the state-of-the-art performance in dynamic scene deblurring not only qualitatively, but also quantitatively.

Key facts. Cited by 722 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2016.

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Citing papers per year: 2020: 1; 2021: 94; 2022: 106; 2023: 98; 2024: 160; 2025: 153; 2026: 110.

722 papers.

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  8. 2021Rethinking Coarse-to-Fine Approach in Single Image Deblurringcited by 324
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  10. 2024Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wildcited by 281
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  12. 2021Deblurring via Stochastic Refinementcited by 248
38 more
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  12. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
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  14. 2024InstructIR: High-Quality Image Restoration Following Human Instructionscited by 139
  15. 2021DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentationcited by 126
  16. 20223D Common Corruptions and Data Augmentationcited by 120
  17. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 119
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  19. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  20. 2023Controlling Vision-Language Models for Multi-Task Image Restorationcited by 108
  21. 2024AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulationcited by 108
  22. 2022IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolationcited by 107
  23. 2022VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolutioncited by 101
  24. 2023AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusioncited by 98
  25. 2022LEDNet: Joint Low-light Enhancement and Deblurring in the Darkcited by 95
  26. 2022Deep Image Deblurring: A Surveycited by 94
  27. 2023Hierarchical Integration Diffusion Model for Realistic Image Deblurringcited by 87
  28. 2021Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priorscited by 82
  29. 2021Intriguing Findings of Frequency Selection for Image Deblurringcited by 80
  30. 2022BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fieldscited by 80
  31. 2021Spatially-Adaptive Image Restoration using Distortion-Guided Networkscited by 77
  32. 2023ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restorationcited by 77
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  34. 2024Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Modelcited by 73
  35. 2024Complexity Experts are Task-Discriminative Learners for Any Image Restorationcited by 72
  36. 2022DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priorscited by 70
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  38. 2023Residual Denoising Diffusion Modelscited by 68

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