mlsanityData to 7 Oct 2026

Residual Dense Network for Image Super-Resolution

arXiv 1802.08797 ↗First posted 24 Feb 2018cs.CVcited by 796

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Cited by 66 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Extensive experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods.

Key facts. Cited by 796 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 167; 2022: 186; 2023: 133; 2024: 148; 2025: 109; 2026: 53.

796 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  2. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
  3. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
  4. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 524
  5. 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
  6. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
  7. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  8. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 301
  9. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 243
  10. 2022VRT: A Video Restoration Transformercited by 219
  11. 2021Unsupervised Degradation Representation Learning for Blind Super-Resolutioncited by 177
  12. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
38 more
  1. 2021Transformer for Single Image Super-Resolutioncited by 150
  2. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  3. 2021Local Texture Estimator for Implicit Representation Functioncited by 126
  4. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  5. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  6. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  7. 2022Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priorscited by 98
  8. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  9. 2024Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approachcited by 93
  10. 2024MambaIRv2: Attentive State Space Restorationcited by 90
  11. 2023Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Modelscited by 86
  12. 2021Deep Burst Super-Resolutioncited by 83
  13. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  14. 2021Intriguing Findings of Frequency Selection for Image Deblurringcited by 80
  15. 2021MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolutioncited by 79
  16. 2022NAFSSR: Stereo Image Super-Resolution Using NAFNetcited by 75
  17. 2021Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescalingcited by 73
  18. 2022GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifoldscited by 73
  19. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 72
  20. 2022CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolutioncited by 69
  21. 2021Robust Reference-based Super-Resolution via C2-Matchingcited by 68
  22. 2022SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolutioncited by 67
  23. 2021Real-World Single Image Super-Resolution: A Brief Reviewcited by 65
  24. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 64
  25. 2024Efficient Diffusion Model for Image Restoration by Residual Shiftingcited by 64
  26. 2024Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionarycited by 63
  27. 2023Omni Aggregation Networks for Lightweight Image Super-Resolutioncited by 62
  28. 2023GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditionscited by 62
  29. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 56
  30. 2023A Comparative Study of Image Restoration Networks for General Backbone Network Designcited by 56
  31. 2023EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolutioncited by 56
  32. 2022Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networkscited by 55
  33. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  34. 2023Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolutioncited by 51
  35. 2025DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolutioncited by 50
  36. 2024Adversarial Diffusion Compression for Real-World Image Super-Resolutioncited by 49
  37. 2021Exploiting Explanations for Model Inversion Attackscited by 48
  38. 2023Super-Resolution Neural Operatorcited by 48

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