mlsanityData to 7 Oct 2026

Accelerating the Super-Resolution Convolutional Neural Network

arXiv 1608.00367 ↗First posted 1 Aug 2016cs.CVcited by 393

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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. However, the high computational cost still hinders it from practical usage that demands real-time performance (24 fps). In this paper, we aim at accelerating the current SRCNN, and propose a compact hourglass-shape CNN structure for faster and better SR. We re-design the SRCNN structure mainly in three aspects. First, we introduce a deconvolution layer at the end of the network, then the mapping is learned directly from the original low-resolution image (without interpolation) to the high-resolution one. Second, we reformulate the mapping layer by shrinking the input feature dimension before mapping and expanding back afterwards. Third, we adopt smaller filter sizes but more mapping layers. The proposed model achieves a speed up of more than 40 times with even superior restoration quality. Further, we present the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance. A corresponding transfer strategy is also proposed for fast training and testing across different upscaling factors.

Key facts. Cited by 393 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 2; 2021: 100; 2022: 89; 2023: 76; 2024: 60; 2025: 39; 2026: 27.

393 papers.

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  1. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  2. 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 486
  3. 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
  4. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  5. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  6. 2022Dataset Condensation via Efficient Synthetic-Data Parameterizationcited by 187
  7. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  8. 2021Investigating Tradeoffs in Real-World Video Super-Resolutioncited by 133
  9. 2022Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolutioncited by 114
  10. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  11. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
  12. 2024VmambaIR: Visual State Space Model for Image Restorationcited by 90
38 more
  1. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  2. 2023Data Distillation: A Surveycited by 86
  3. 2023Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Modelscited by 86
  4. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  5. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  6. 2024Diffusion Models in Low-Level Vision: A Surveycited by 73
  7. 2022SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolutioncited by 67
  8. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 64
  9. 2021Are we ready for a new paradigm shift? A Survey on Visual Deep MLPcited by 63
  10. 2024Diffusion Models, Image Super-Resolution And Everything: A Surveycited by 63
  11. 2023Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolutioncited by 56
  12. 2024Towards Realistic Data Generation for Real-World Super-Resolutioncited by 56
  13. 2022Residual Local Feature Network for Efficient Super-Resolutioncited by 53
  14. 2023Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolutioncited by 51
  15. 2021NTIRE 2021 Challenge on Image Deblurringcited by 49
  16. 2023Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wildcited by 46
  17. 2022Blueprint Separable Residual Network for Efficient Image Super-Resolutioncited by 44
  18. 2022ShuffleMixer: An Efficient ConvNet for Image Super-Resolutioncited by 44
  19. 2023DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Modelscited by 41
  20. 2024XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolutioncited by 41
  21. 2022NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Resultscited by 40
  22. 2025One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillationcited by 40
  23. 2022GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priorscited by 39
  24. 2021Text Prior Guided Scene Text Image Super-resolutioncited by 37
  25. 2021Learning Frequency-aware Dynamic Network for Efficient Super-Resolutioncited by 36
  26. 2024Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolutioncited by 36
  27. 2021Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 34
  28. 2021Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Searchcited by 34
  29. 2023Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolutioncited by 34
  30. 2025Harnessing Diffusion-Yielded Score Priors for Image Restorationcited by 34
  31. 2022Revisiting RCAN: Improved Training for Image Super-Resolutioncited by 32
  32. 2021Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devicescited by 30
  33. 2021A Practical Contrastive Learning Framework for Single-Image Super-Resolutioncited by 30
  34. 2023Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flowscited by 30
  35. 2021Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolutioncited by 29
  36. 2022Multi-scale Attention Network for Single Image Super-Resolutioncited by 29
  37. 2023Unifying Image Processing as Visual Prompting Question Answeringcited by 29
  38. 2022Learning the Degradation Distribution for Blind Image Super-Resolutioncited by 27

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