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Deeply-Recursive Convolutional Network for Image Super-Resolution

arXiv 1511.04491 ↗First posted 14 Nov 2015cs.CV cs.LGcited by 317

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Abstract (arXiv)

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive-supervision and skip-connection. Our method outperforms previous methods by a large margin.

Key facts. Cited by 317 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2015.

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Citing papers per year: 2020: 1; 2021: 94; 2022: 83; 2023: 48; 2024: 40; 2025: 32; 2026: 19.

317 papers.

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  1. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  2. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  3. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
  4. 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
  5. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  6. 2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 198
  7. 2021Unsupervised Degradation Representation Learning for Blind Super-Resolutioncited by 177
  8. 2021Transformer for Single Image Super-Resolutioncited by 150
  9. 2022Learning Enriched Features for Fast Image Restoration and Enhancementcited by 150
  10. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  11. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  12. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
38 more
  1. 2024Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approachcited by 93
  2. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  3. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  4. 2021MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolutioncited by 79
  5. 2021Robust Reference-based Super-Resolution via C2-Matchingcited by 68
  6. 2022SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolutioncited by 67
  7. 2021Burst Image Restoration and Enhancementcited by 64
  8. 2021Lite Vision Transformer with Enhanced Self-Attentioncited by 64
  9. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 64
  10. 2024Diffusion Models, Image Super-Resolution And Everything: A Surveycited by 63
  11. 2024Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionarycited by 63
  12. 2023Omni Aggregation Networks for Lightweight Image Super-Resolutioncited by 62
  13. 2023C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detectioncited by 57
  14. 2023Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolutioncited by 56
  15. 2022Multimodal learning with graphscited by 54
  16. 2022Residual Local Feature Network for Efficient Super-Resolutioncited by 53
  17. 2021An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representationcited by 47
  18. 2022Deep Constrained Least Squares for Blind Image Super-Resolutioncited by 47
  19. 2023Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wildcited by 46
  20. 2022Blueprint Separable Residual Network for Efficient Image Super-Resolutioncited by 44
  21. 2022ShuffleMixer: An Efficient ConvNet for Image Super-Resolutioncited by 44
  22. 2021Blind Image Super-Resolution via Contrastive Representation Learningcited by 32
  23. 2022BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignmentcited by 32
  24. 2021Best-Buddy GANs for Highly Detailed Image Super-Resolutioncited by 30
  25. 2024CAMixerSR: Only Details Need More "Attention"cited by 30
  26. 2021Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolutioncited by 29
  27. 2022Multi-scale Attention Network for Single Image Super-Resolutioncited by 29
  28. 2022Fast and Memory-Efficient Network Towards Efficient Image Super-Resolutioncited by 27
  29. 2021COMISR: Compression-Informed Video Super-Resolutioncited by 26
  30. 2021Finding Discriminative Filters for Specific Degradations in Blind Super-Resolutioncited by 26
  31. 2021Sliced Recursive Transformercited by 26
  32. 2022DRT: A Lightweight Single Image Deraining Recursive Transformercited by 26
  33. 2024SRGS: Super-Resolution 3D Gaussian Splattingcited by 26
  34. 2022Memory-augmented Deep Unfolding Network for Guided Image Super-resolutioncited by 25
  35. 2025Progressive Focused Transformer for Single Image Super-Resolutioncited by 25
  36. 2021Anchor-based Plain Net for Mobile Image Super-Resolutioncited by 24
  37. 2021Towards Compact Single Image Super-Resolution via Contrastive Self-distillationcited by 24
  38. 2023Swift Parameter-free Attention Network for Efficient Super-Resolutioncited by 24

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