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https://github.com/ChaofWang/Awesome-Super-Resolution

Collect super-resolution related papers, data, repositories
https://github.com/ChaofWang/Awesome-Super-Resolution

List: Awesome-Super-Resolution

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Collect super-resolution related papers, data, repositories

Awesome Lists containing this project

README 

        
# Quick navigation



- [repositories](awesome_paper_list_and_repos.md)

- [Datasets](dataset.md)

- [papers](#papers)

  - [Non-DL based approach](non_dl_papers.md)

  - [DL based approach](#DL-based-approach)

    - [2014-2016](2014-2016_papers.md)

    - [2017](2017_papers.md)

    - [2018](2018_papers.md)

    - [2019](2019_papers.md)

    - [2020](2020_papers.md)

    - [2021](2021_papers.md)

    - [2022](2022_papers.md)

    - [2023](2023_papers.md)

    - [2024](#2024)

- [Super Resolution workshop papers](workshops.md)

- [Super Resolution survey](sr_survey.md)



# Awesome-Super-Resolution（in progress）



Collect some super-resolution related papers, data and repositories.



## papers



### DL based approach



Note this table is referenced from [here](https://github.com/LoSealL/VideoSuperResolution/blob/master/README.md#network-list-and-reference-updating)



### 2024

More years papers, plase check Quick navigation



| Title                  | Model                  | Published                                                    | Code                                                         | Keywords                                                     |

| ---------------------- | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |

|Noise-free Optimization in Early Training Steps for Image Super-Resolution | ECO | [AAAI24](https://arxiv.org/pdf/2312.17526.pdf) | [code](https://github.com/2minkyulee/ECO) |SISR, train opt |

|Transforming Image Super-Resolution:A ConvFormer-based Efficient Approach |CFSR | [arxiv](https://arxiv.org/pdf/2401.05633.pdf) | [code](https://github.com/Aitical/CFSR) | |

|Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention |MIA-VSR | [arxiv](https://arxiv.org/pdf/2401.06312.pdf) | [code](https://github.com/LabShuHangGU/MIA-VSR) | |

|Efficient Image Super-Resolution via Symmetric Visual Attention Network |SVAN | [arxiv](https://browse.arxiv.org/pdf/2401.08913.pdf) |  | |

|You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation |YONOS-SR | [arxiv](https://arxiv.org/pdf/2401.17258.pdf) |  | |

|See More Details: Efficient Image Super-Resolution by Experts Mining |SeemoRe | [arxiv](https://arxiv.org/pdf/2402.03412.pdf) |[code](https://github.com/eduardzamfir/seemoredetails)  |  Efficient SR|

|SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution |SAM-DiffSR | [arxiv](https://arxiv.org/pdf/2402.17133.pdf) |[code](https://github.com/lose4578/SAM-DiffSR)  | |

|CAMixerSR: Only Details Need More “Attention” |CAMixerSR | [CVPR24](https://arxiv.org/pdf/2402.19289.pdf) |[code](https://github.com/icandle/CAMixerSR)  | |

|SeD: Semantic-Aware Discriminator for Image Super-Resolution |SeD | [CVPR24](https://arxiv.org/pdf/2402.19387.pdf) |[code](https://github.com/lbc12345/SeD)  | |

|Text-guided Explorable Image Super-resolution |- | [CVPR24](https://arxiv.org/pdf/2403.01124.pdf) |  | |

|Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning |LWay | [CVPR24](https://arxiv.org/pdf/2403.02601.pdf) |[code](https://haoyuchen.com/LWay)  | |

|APISR: Anime Production Inspired Real-World Anime Super-Resolution |APISR | [CVPR24](https://arxiv.org/pdf/2403.01598.pdf) |[code](https://github.com/Kiteretsu77/APISR)  | |

|XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution |XPSR | [arxiv](https://arxiv.org/pdf/2403.05049.pdf) |[code](https://github.com/qyp2000/XPSR)  | |

|Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary |ATD | [CVPR24](https://arxiv.org/pdf/2401.08209.pdf) |[code](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary)  | |

|Boosting Flow-based Generative Super-Resolution Models via Learned Prior |FlowSR-LP | [CVPR24](https://arxiv.org/pdf/2403.10988.pdf) |[code](https://github.com/liyuantsao/FlowSR-LP)  | |

|Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder | | [CVPR24](https://arxiv.org/pdf/2403.10255.pdf) | | |

|CasSR: Activating Image Power for Real-World Image Super-Resolution |CasSR | [arxiv](https://arxiv.org/pdf/2403.11451.pdf) |  | |

|VmambaIR: Visual State Space Model for Image Restoration |VmambaIR | [arxiv](https://arxiv.org/pdf/2403.11423.pdf) | [code](https://github.com/AlphacatPlus/VmambaIR) | |

|Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-Resolution |DDIR | [arxiv](https://arxiv.org/pdf/2403.10925.pdf) |  | |

|Activating Wider Areas in Image Super-Resolution |MMA | [arxiv](https://arxiv.org/pdf/2403.08330.pdf) |  | |

|Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution |SSR | [arxiv](https://arxiv.org/pdf/2403.05808.pdf) |  | |

|AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation |AddSR | [arxiv](https://arxiv.org/pdf/2404.01717.pdf) |[code](https://nju-pcalab.github.io/projects/AddSR/)  | |

|Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss |SR4IR | [CVPR24](https://arxiv.org/pdf/2404.01692.pdf) |[code](https://github.com/JaehaKim97/SR4IR) | |

|RefQSR: Reference-based Quantization for Image Super-Resolution Networks |RefQSR | [TIP](https://arxiv.org/pdf/2404.01690.pdf) |  | |

|DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion |DeeDSR | [arxiv](https://arxiv.org/pdf/2404.00661.pdf) |[code](https://github.com/bichunyang419/DeeDSR)  | |

|Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution |SATeCo | [CVPR24](https://arxiv.org/pdf/2403.17000.pdf) | | |

|CFAT: Unleashing Triangular Windows for Image Super-resolution |CFAT | [CVPR24](https://arxiv.org/pdf/2403.16143.pdf) |[code](https://github.com/rayabhisek123/CFAT) | |

|Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution |SARGD | [arxiv](https://arxiv.org/pdf/2403.16643) |[code](https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion) | |

|DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion |DeeDSR | [arxiv](https://arxiv.org/pdf/2404.00661) |[code](https://github.com/bichunyang419/DeeDSR) | |

|Video Interpolation with Diffusion Models|VIDIM | [CVPR 2024](https://arxiv.org/pdf/2404.01203) |[web](https://vidim-interpolation.github.io/) | |

|Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution|MiPKD | [arxiv](https://arxiv.org/pdf/2404.02573) |  | |

|AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution|AdaBM | [CVPR24](https://arxiv.org/pdf/2404.02573) |[code](https://github.com/Cheeun/AdaBM)  | |

|Collaborative Feedback Discriminative Propagation for Video Super-Resolution|CFDVSR | [arxiv](https://arxiv.org/pdf/2404.04745) |[code](https://github.com/House-Leo/CFDVSR)  | |

|Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution|DiffMSR | [CVPR24](https://arxiv.org/pdf/2404.04785) |[code](https://github.com/GuangYuanKK/DiffMSR)  | |

|LIPT: Latency-aware Image Processing Transformer|LIPT | [arxiv](https://arxiv.org/pdf/2404.06075) | | |

|MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution|MTKD | [arxiv](https://arxiv.org/pdf/2404.09571) | | |

|OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model| OmniSSR | [arxiv](https://arxiv.org/pdf/2404.10312) | | |

|Partial Large Kernel CNNs for Efficient Super-Resolution| PLKSR | [arxiv](https://arxiv.org/pdf/2404.11848) |[code](https://github.com/dslisleedh/PLKSR) | |

|VideoGigaGAN: Towards Detail-rich Video Super-Resolution| VideoGigaGAN | [arxiv](https://arxiv.org/pdf/2404.12388) |[web](https://videogigagan.github.io/) | |

|A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution| DKP | [CVPR24](https://arxiv.org/pdf/2404.15620) |[code](https://github.com/XYLGroup/DKP) | |

|Latent Modulated Function for Computational Optimal Continuous Image Representation| LMF | [arxiv](https://arxiv.org/pdf/2404.16451) |[code](https://github.com/HeZongyao/LMF) | |

|A Study in Dataset Pruning for Image Super-Resolution|   | [ICANN 2024](https://arxiv.org/pdf/2403.17083) |  | |

|CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution| CDFormer | [CVPR24](https://arxiv.org/pdf/2405.07648) |[code](https://github.com/I2-Multimedia-Lab/CDFormer) | |

|Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution| ERealSR-DGPNet | [arxiv](https://arxiv.org/pdf/2405.07023) |[code](https://github.com/peylnog/ERealSR-DGPNet) | |

|Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer| Inf-DiT | [arxiv](https://arxiv.org/pdf/2405.04312) |[code](https://github.com/THUDM/Inf-DiT) | |

|Bilateral Event Mining and Complementary for Event Stream Super-Resolution| BMCNet-ESR | [CVPR24](https://arxiv.org/pdf/2405.10037) |[code](https://github.com/Lqm26/BMCNet-ESR) | |

|Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution| FDDiff | [arxiv](https://arxiv.org/pdf/2405.10014) | | |

|Exploring the Low-Pass Filtering Behavior in Image Super-Resolution| LPFInISR | [ICML24](https://arxiv.org/pdf/2405.07919) |[code](https://github.com/RisingEntropy/LPFInISR) | |

|Does Diffusion Beat GAN in Image Super Resolution?| gan_vs_diff_sr | [arxiv](https://arxiv.org/pdf/2405.17261) |[code](https://github.com/yandex-research/gan_vs_diff_sr) | |

|Perceptual Fairness in Image Restoration|   | [arxiv](https://arxiv.org/pdf/2405.13805) | | |

|PatchScaler: An Efficient Patch-Independent Diffusion Model for Super-Resolution| PatchScaler | [arxiv](https://arxiv.org/pdf/2405.17158) |[code](https://github.com/yongliuy/PatchScaler) | |

|Image Processing GNN: Breaking Rigidity in Super-Resolution|IPG | [CVPR24](https://openaccess.thecvf.com/content/CVPR2024/papers/Tian_Image_Processing_GNN_Breaking_Rigidity_in_Super-Resolution_CVPR_2024_paper.pdf) |[code](https://github.com/huawei-noah/Efficient-Computing/tree/master/LowLevel/IPG)  | |