mlsanityData to 13 Oct 2026

Enhanced Deep Residual Networks for Single Image Super-Resolution

arXiv 1707.02921 ↗First posted 10 Jul 2017cs.CVcited by 1,549

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Cited by 157 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.

Key facts. Cited by 1,549 papers in the map (data through 13 Oct 2026). First posted on arXiv 10 Jul 2017.

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Citing papers per year: 2020: 2; 2021: 272; 2022: 277; 2023: 261; 2024: 286; 2025: 259; 2026: 192.

1,549 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,876
  2. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,622
  3. 2021Transformers in Vision: A Surveycited by 1,010
  4. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 965
  5. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 865
  6. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 527
  7. 2022Activating More Pixels in Image Super-Resolution Transformercited by 435
  8. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  9. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 350
  10. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 348
  11. 2021Rethinking Coarse-to-Fine Approach in Single Image Deblurringcited by 324
  12. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 315
38 more
  1. 2023Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylizationcited by 253
  2. 2021Deblurring via Stochastic Refinementcited by 249
  3. 2024One-Step Effective Diffusion Network for Real-World Image Super-Resolutioncited by 245
  4. 2021HINet: Half Instance Normalization Network for Image Restorationcited by 236
  5. 2023Implicit Diffusion Models for Continuous Super-Resolutioncited by 217
  6. 2021GAN Prior Embedded Network for Blind Face Restoration in the Wildcited by 197
  7. 2023Image Restoration with Mean-Reverting Stochastic Differential Equationscited by 183
  8. 2024A Survey on Diffusion Models for Inverse Problemscited by 179
  9. 2021Unsupervised Degradation Representation Learning for Blind Super-Resolutioncited by 177
  10. 2022VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolutioncited by 175
  11. 2022Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Methodcited by 174
  12. 2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 165
  13. 2023Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priorscited by 156
  14. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  15. 2021Transformer for Single Image Super-Resolutioncited by 150
  16. 2022Learning Enriched Features for Fast Image Restoration and Enhancementcited by 150
  17. 2023Efficient and Explicit Modelling of Image Hierarchies for Image Restorationcited by 145
  18. 2021Local Texture Estimator for Implicit Representation Functioncited by 127
  19. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  20. 2022VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decodercited by 116
  21. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  22. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 109
  23. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 108
  24. 2022Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priorscited by 100
  25. 2023AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusioncited by 99
  26. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  27. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
  28. 2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 93
  29. 2022Cross Aggregation Transformer for Image Restorationcited by 92
  30. 2024MambaIRv2: Attentive State Space Restorationcited by 92
  31. 2024VmambaIR: Visual State Space Model for Image Restorationcited by 90
  32. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  33. 2022Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesiscited by 86
  34. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  35. 2021Deep Burst Super-Resolutioncited by 83
  36. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  37. 2021MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolutioncited by 79
  38. 2023DR2: Diffusion-based Robust Degradation Remover for Blind Face Restorationcited by 79

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