mlsanityData to 7 Oct 2026

Deep Stacked Hierarchical Multi-patch Network for Image Deblurring

arXiv 1904.03468 ↗First posted 6 Apr 2019cs.CVcited by 205

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Abstract (arXiv)

Despite deep end-to-end learning methods have shown their superiority in removing non-uniform motion blur, there still exist major challenges with the current multi-scale and scale-recurrent models: 1) Deconvolution/upsampling operations in the coarse-to-fine scheme result in expensive runtime; 2) Simply increasing the model depth with finer-scale levels cannot improve the quality of deblurring. To tackle the above problems, we present a deep hierarchical multi-patch network inspired by Spatial Pyramid Matching to deal with blurry images via a fine-to-coarse hierarchical representation. To deal with the performance saturation w.r.t. depth, we propose a stacked version of our multi-patch model. Our proposed basic multi-patch model achieves the state-of-the-art performance on the GoPro dataset while enjoying a 40x faster runtime compared to current multi-scale methods. With 30ms to process an image at 1280x720 resolution, it is the first real-time deep motion deblurring model for 720p images at 30fps. For stacked networks, significant improvements (over 1.2dB) are achieved on the GoPro dataset by increasing the network depth. Moreover, by varying the depth of the stacked model, one can adapt the performance and runtime of the same network for different application scenarios.

Key facts. Cited by 205 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2019.

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Citing papers per year: 2021: 43; 2022: 56; 2023: 32; 2024: 47; 2025: 18; 2026: 9.

205 papers.

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  1. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  2. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
  3. 2021Multi-Stage Progressive Image Restorationcited by 813
  4. 2021Rethinking Coarse-to-Fine Approach in Single Image Deblurringcited by 324
  5. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 301
  6. 2021Ensemble deep learning: A reviewcited by 283
  7. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  8. 2021Deblurring via Stochastic Refinementcited by 248
  9. 2021HINet: Half Instance Normalization Network for Image Restorationcited by 236
  10. 2022VRT: A Video Restoration Transformercited by 219
  11. 2022Recurrent Video Restoration Transformer with Guided Deformable Attentioncited by 168
  12. 2022Efficient Frequency Domain-based Transformers for High-Quality Image Deblurringcited by 164
38 more
  1. 2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 164
  2. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  3. 2022Stripformer: Strip Transformer for Fast Image Deblurringcited by 139
  4. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 119
  5. 2021Event-Based Fusion for Motion Deblurring with Cross-modal Attentioncited by 112
  6. 2022LEDNet: Joint Low-light Enhancement and Deblurring in the Darkcited by 95
  7. 2022Deep Image Deblurring: A Surveycited by 94
  8. 2023Hierarchical Integration Diffusion Model for Realistic Image Deblurringcited by 87
  9. 2021Intriguing Findings of Frequency Selection for Image Deblurringcited by 80
  10. 2021Spatially-Adaptive Image Restoration using Distortion-Guided Networkscited by 77
  11. 2022Multiscale Structure Guided Diffusion for Image Deblurringcited by 76
  12. 2022DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priorscited by 70
  13. 2023Residual Denoising Diffusion Modelscited by 68
  14. 2024Deblurring 3D Gaussian Splattingcited by 66
  15. 2021Improving Image Restoration by Revisiting Global Information Aggregationcited by 60
  16. 2022Realistic Blur Synthesis for Learning Image Deblurringcited by 51
  17. 2021NTIRE 2021 Challenge on Image Deblurringcited by 49
  18. 2021BANet: Blur-aware Attention Networks for Dynamic Scene Deblurringcited by 44
  19. 2024MaIR: A Locality- and Continuity-Preserving Mamba for Image Restorationcited by 43
  20. 2022Learning Dual Memory Dictionaries for Blind Face Restorationcited by 40
  21. 2024Correlation Matching Transformation Transformers for UHD Image Restorationcited by 39
  22. 2021DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurringcited by 36
  23. 2023Improving Image Restoration through Removing Degradations in Textual Representationscited by 36
  24. 2025MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restorationcited by 36
  25. 2022Learning Degradation Representations for Image Deblurringcited by 35
  26. 2022MSSNet: Multi-Scale-Stage Network for Single Image Deblurringcited by 31
  27. 2022Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireingcited by 30
  28. 2021MC-Blur: A Comprehensive Benchmark for Image Deblurringcited by 27
  29. 2024Efficient Visual State Space Model for Image Deblurringcited by 27
  30. 2021SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurringcited by 26
  31. 2021Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurringcited by 25
  32. 2021Event-guided Deblurring of Unknown Exposure Time Videoscited by 25
  33. 2024AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurringcited by 25
  34. 2023DiffI2I: Efficient Diffusion Model for Image-to-Image Translationcited by 24
  35. 2023Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurringcited by 24
  36. 2024CU-Mamba: Selective State Space Models with Channel Learning for Image Restorationcited by 24
  37. 2023Image Super-Resolution with Text Prompt Diffusioncited by 22
  38. 2024UniProcessor: A Text-induced Unified Low-level Image Processorcited by 22

50 most cited of 205 All 205 (JSON)