mlsanityData to 7 Oct 2026

Video Super-Resolution with Recurrent Structure-Detail Network

arXiv 2008.00455 ↗First posted 2 Aug 2020cs.CVcited by 116

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Abstract (arXiv)

Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the recurrent-based methods. In this work, we propose a novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame. It divides the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks. In addition, a hidden state adaptation module that allows the current frame to selectively use information from hidden state is introduced to enhance its robustness to appearance change and error accumulation. Extensive ablation study validate the effectiveness of the proposed modules. Experiments on several benchmark datasets demonstrate the superior performance of the proposed method compared to state-of-the-art methods on video super-resolution.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 38; 2023: 15; 2024: 12; 2025: 22; 2026: 5.

116 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  2. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 340
  3. 2022VRT: A Video Restoration Transformercited by 219
  4. 2022Recurrent Video Restoration Transformer with Guided Deformable Attentioncited by 168
  5. 2021Investigating Tradeoffs in Real-World Video Super-Resolutioncited by 133
  6. 2023Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolutioncited by 131
  7. 2021Video Super-Resolution Transformercited by 120
  8. 2022VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolutioncited by 101
  9. 2021Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescalingcited by 73
  10. 2022CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolutioncited by 69
  11. 2022A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shiftcited by 68
  12. 2024VEnhancer: Generative Space-Time Enhancement for Video Generationcited by 64
38 more
  1. 2025SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restorationcited by 62
  2. 2022Learning Trajectory-Aware Transformer for Video Super-Resolutioncited by 61
  3. 2025STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolutioncited by 56
  4. 2021Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolutioncited by 52
  5. 2021Multi-Target Domain Adaptation with Collaborative Consistency Learningcited by 43
  6. 2023Enhancing Video Super-Resolution via Implicit Resampling-based Alignmentcited by 35
  7. 2024DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Modelscited by 33
  8. 2024RealViformer: Investigating Attention for Real-World Video Super-Resolutioncited by 33
  9. 2021Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imagingcited by 31
  10. 2021Omniscient Video Super-Resolutioncited by 30
  11. 2024VideoGigaGAN: Towards Detail-rich Video Super-Resolutioncited by 30
  12. 2024Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attentioncited by 29
  13. 2021COMISR: Compression-Informed Video Super-Resolutioncited by 26
  14. 2022Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolutioncited by 25
  15. 2021Revisiting Temporal Alignment for Video Restorationcited by 24
  16. 2022Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Onescited by 24
  17. 2022AnimeSR: Learning Real-World Super-Resolution Models for Animation Videoscited by 23
  18. 2024Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolutioncited by 23
  19. 2022Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolutioncited by 22
  20. 2022Basic Binary Convolution Unit for Binarized Image Restoration Networkcited by 22
  21. 2022Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advancescited by 21
  22. 2023Can SAM Boost Video Super-Resolution?cited by 21
  23. 2022Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modelingcited by 20
  24. 2022AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Resultscited by 20
  25. 2022Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolutioncited by 20
  26. 2021FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolutioncited by 19
  27. 2021Self-Conditioned Probabilistic Learning of Video Rescalingcited by 19
  28. 2022Reference-based Video Super-Resolution Using Multi-Camera Video Tripletscited by 19
  29. 2025PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancementcited by 17
  30. 2022Temporal Consistency Learning of inter-frames for Video Super-Resolutioncited by 16
  31. 2021NTIRE 2021 Challenge on Video Super-Resolutioncited by 15
  32. 2022Structured Sparsity Learning for Efficient Video Super-Resolutioncited by 14
  33. 2024Learning Truncated Causal History Model for Video Restorationcited by 13
  34. 2022Online Video Super-Resolution with Convolutional Kernel Bypass Graftcited by 12
  35. 2025Show and Polish: Reference-Guided Identity Preservation in Face Video Restorationcited by 12
  36. 2022Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restorationcited by 11
  37. 2021Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsamplingcited by 9
  38. 2023A Dive into SAM Prior in Image Restorationcited by 9

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