mlsanityData to 14 Oct 2026

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

arXiv 1807.02758 ↗First posted 8 Jul 2018cs.CVcited by 1,264

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Cited by 102 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.

Key facts. Cited by 1,264 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jul 2018.

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Citing papers per year: 2020: 1; 2021: 233; 2022: 246; 2023: 236; 2024: 229; 2025: 187; 2026: 132.

1,264 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  3. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  4. 2021Transformers in Vision: A Surveycited by 1,013
  5. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
  6. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 866
  7. 2021Multi-Stage Progressive Image Restorationcited by 816
  8. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 529
  9. 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 489
  10. 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
  11. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  12. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 352
38 more
  1. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 349
  2. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 315
  3. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 255
  4. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 246
  5. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  6. 2022VRT: A Video Restoration Transformercited by 220
  7. 2021Unsupervised Degradation Representation Learning for Blind Super-Resolutioncited by 177
  8. 2022VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolutioncited by 176
  9. 2022Recurrent Video Restoration Transformer with Guided Deformable Attentioncited by 170
  10. 2023Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generationcited by 154
  11. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  12. 2021Transformer for Single Image Super-Resolutioncited by 150
  13. 2022Learning Enriched Features for Fast Image Restoration and Enhancementcited by 150
  14. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  15. 2021Local Texture Estimator for Implicit Representation Functioncited by 127
  16. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  17. 2021Video Super-Resolution Transformercited by 121
  18. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 121
  19. 2021Uncertainty-aware Joint Salient Object and Camouflaged Object Detectioncited by 114
  20. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  21. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 109
  22. 2022Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priorscited by 100
  23. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  24. 2024MambaIRv2: Attentive State Space Restorationcited by 94
  25. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
  26. 2022Cross Aggregation Transformer for Image Restorationcited by 92
  27. 2023Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Groupingcited by 92
  28. 2024VmambaIR: Visual State Space Model for Image Restorationcited by 90
  29. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  30. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  31. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  32. 2021MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolutioncited by 80
  33. 2022High-resolution Iterative Feedback Network for Camouflaged Object Detectioncited by 78
  34. 2022NAFSSR: Stereo Image Super-Resolution Using NAFNetcited by 76
  35. 2021Flow-based Kernel Prior with Application to Blind Super-Resolutioncited by 74
  36. 2021Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescalingcited by 73
  37. 2022GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifoldscited by 73
  38. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73

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