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Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network

arXiv 1803.08664 ↗First posted 23 Mar 2018cs.CVcited by 264

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Abstract (arXiv)

In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.

Key facts. Cited by 264 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Mar 2018.

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Citing papers per year: 2020: 1; 2021: 41; 2022: 66; 2023: 50; 2024: 53; 2025: 35; 2026: 18.

264 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
  2. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 352
  3. 2021Unsupervised Degradation Representation Learning for Blind Super-Resolutioncited by 177
  4. 2022Efficient Long-Range Attention Network for Image Super-resolutioncited by 151
  5. 2021Transformer for Single Image Super-Resolutioncited by 150
  6. 2022Learning Enriched Features for Fast Image Restoration and Enhancementcited by 150
  7. 2022Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolutioncited by 96
  8. 2024MambaIRv2: Attentive State Space Restorationcited by 94
  9. 2021LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyondcited by 88
  10. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  11. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  12. 2021Burst Image Restoration and Enhancementcited by 64
38 more
  1. 2024Diffusion Models, Image Super-Resolution And Everything: A Surveycited by 64
  2. 2024Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionarycited by 64
  3. 2023Omni Aggregation Networks for Lightweight Image Super-Resolutioncited by 62
  4. 2023Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolutioncited by 57
  5. 2022Residual Local Feature Network for Efficient Super-Resolutioncited by 53
  6. 2022Deep Constrained Least Squares for Blind Image Super-Resolutioncited by 47
  7. 2022N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolutioncited by 46
  8. 2023Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wildcited by 46
  9. 2024Lightweight Adaptive Feature De-drifting for Compressed Image Classificationcited by 46
  10. 2022ShuffleMixer: An Efficient ConvNet for Image Super-Resolutioncited by 45
  11. 2024MaIR: A Locality- and Continuity-Preserving Mamba for Image Restorationcited by 45
  12. 2022Blueprint Separable Residual Network for Efficient Image Super-Resolutioncited by 44
  13. 2024HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolutioncited by 42
  14. 2021Learning Frequency-aware Dynamic Network for Efficient Super-Resolutioncited by 36
  15. 2023OTOV2: Automatic, Generic, User-Friendlycited by 32
  16. 2021Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolutioncited by 29
  17. 2022Fast and Memory-Efficient Network Towards Efficient Image Super-Resolutioncited by 27
  18. 2022CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolutioncited by 27
  19. 2023GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generationcited by 25
  20. 2025Progressive Focused Transformer for Single Image Super-Resolutioncited by 25
  21. 2021Anchor-based Plain Net for Mobile Image Super-Resolutioncited by 24
  22. 2021Towards Compact Single Image Super-Resolution via Contrastive Self-distillationcited by 24
  23. 2023Swift Parameter-free Attention Network for Efficient Super-Resolutioncited by 24
  24. 2024DVMSR: Distillated Vision Mamba for Efficient Super-Resolutioncited by 24
  25. 2021A Comprehensive Review of Deep Learning-based Single Image Super-resolutioncited by 23
  26. 2024See More Details: Efficient Image Super-Resolution by Experts Miningcited by 23
  27. 2022You Only Cut Once: Boosting Data Augmentation with a Single Cutcited by 22
  28. 2022Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformercited by 22
  29. 2022Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advancescited by 22
  30. 2022Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networkscited by 21
  31. 2022Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimationcited by 21
  32. 2021Attention in Attention Network for Image Super-Resolutioncited by 20
  33. 2021A Two-Stage Attentive Network for Single Image Super-Resolutioncited by 20
  34. 2022Self-Calibrated Efficient Transformer for Lightweight Super-Resolutioncited by 20
  35. 2022Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolutioncited by 20
  36. 2021Collapsible Linear Blocks for Super-Efficient Super Resolutioncited by 19
  37. 2021A Systematic Survey of Deep Learning-based Single-Image Super-Resolutioncited by 19
  38. 2021A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolutioncited by 19

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