mlsanityData to 14 Oct 2026

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

arXiv 1712.06116 ↗First posted 17 Dec 2017cs.CVcited by 228

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Abstract (arXiv)

Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to non-blindly deal with multiple degradations. To address these issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.

Key facts. Cited by 228 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 53; 2022: 57; 2023: 43; 2024: 41; 2025: 21; 2026: 13.

228 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 529
  3. 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 365
  4. 2022VRT: A Video Restoration Transformercited by 220
  5. 2021Unsupervised Degradation Representation Learning for Blind Super-Resolutioncited by 177
  6. 2022Recurrent Video Restoration Transformer with Guided Deformable Attentioncited by 170
  7. 2021Transformer for Single Image Super-Resolutioncited by 150
  8. 2023Dual Aggregation Transformer for Image Super-Resolutioncited by 124
  9. 2022Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priorscited by 100
  10. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  11. 2022Deep Image Deblurring: A Surveycited by 94
  12. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
38 more
  1. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  2. 2022Accurate Image Restoration with Attention Retractable Transformercited by 85
  3. 2021Towards Flexible Blind JPEG Artifacts Removalcited by 76
  4. 2021Flow-based Kernel Prior with Application to Blind Super-Resolutioncited by 74
  5. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  6. 2022CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolutioncited by 70
  7. 2024Degradation-Guided One-Step Image Super-Resolution with Diffusion Priorscited by 69
  8. 2024Efficient Diffusion Model for Image Restoration by Residual Shiftingcited by 64
  9. 2023Omni Aggregation Networks for Lightweight Image Super-Resolutioncited by 62
  10. 2021Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolutioncited by 52
  11. 2022Deep Constrained Least Squares for Blind Image Super-Resolutioncited by 47
  12. 2023DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Modelscited by 41
  13. 2024XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolutioncited by 41
  14. 2023Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolutioncited by 36
  15. 2022MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolutioncited by 33
  16. 2021Blind Image Super-Resolution via Contrastive Representation Learningcited by 32
  17. 2024FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolutioncited by 32
  18. 2021Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Networkcited by 31
  19. 2021A Practical Contrastive Learning Framework for Single-Image Super-Resolutioncited by 30
  20. 2022Knowledge Distillation based Degradation Estimation for Blind Super-Resolutioncited by 28
  21. 2024FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurringcited by 28
  22. 2021Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernelcited by 27
  23. 2022Learning the Degradation Distribution for Blind Image Super-Resolutioncited by 27
  24. 2021Finding Discriminative Filters for Specific Degradations in Blind Super-Resolutioncited by 26
  25. 2022Efficient Non-Local Contrastive Attention for Image Super-Resolutioncited by 25
  26. 2025Progressive Focused Transformer for Single Image Super-Resolutioncited by 25
  27. 2022Variational Deep Image Restorationcited by 24
  28. 2024DVMSR: Distillated Vision Mamba for Efficient Super-Resolutioncited by 24
  29. 2021A Comprehensive Review of Deep Learning-based Single Image Super-resolutioncited by 23
  30. 2023Image Super-Resolution with Text Prompt Diffusioncited by 23
  31. 2022A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Boundscited by 22
  32. 2024Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restorationcited by 21
  33. 2021Attention in Attention Network for Image Super-Resolutioncited by 20
  34. 2021A Two-Stage Attentive Network for Single Image Super-Resolutioncited by 20
  35. 2022Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observationscited by 20
  36. 2022Self-Calibrated Efficient Transformer for Lightweight Super-Resolutioncited by 20
  37. 2023Human Guided Ground-truth Generation for Realistic Image Super-resolutioncited by 20
  38. 2021A Systematic Survey of Deep Learning-based Single-Image Super-Resolutioncited by 19

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