Collect super-resolution related papers, data, repositories
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Awesome-Super-Resolution（in progress）
Collect some super-resolution related papers, data and repositories.
repositories
Awesome paper list:
Awesome repos:
| repo | Framework | | :----------------------------------------------------------: | :--------: | | EDSR-PyTorch | PyTorch | | Image-Super-Resolution | Keras | | image-super-resolution | Keras | | Super-Resolution-Zoo | MxNet | | super-resolution | Keras | | neural-enhance | Theano | | srez | Tensorflow | | waifu2x | Torch | | BasicSR | PyTorch | | super-resolution | PyTorch | | VideoSuperResolution | Tensorflow | | video-super-resolution | Pytorch | |MMSR | PyTorch |
Datasets
Note this table is referenced from here.
| Name | Usage | Link | Comments | | :----------: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------: | | Set5 | Test | download | jbhuang0604 | | SET14 | Test | download | jbhuang0604 | | BSD100 | Test | download | jbhuang0604 | | Urban100 | Test | download | jbhuang0604 | | Manga109 | Test | website | | | SunHay80 | Test | download | jbhuang0604 | | BSD300 | Train/Val | download | | | BSD500 | Train/Val | download | | | 91-Image | Train | download | Yang | | DIV2K2017 | Train/Val | website | NTIRE2017 | | Flickr2K | Train | download | | | Real SR | Train/Val | website | NTIRE2019 | | Waterloo | Train | website | | | VID4 | Test | download | 4 videos | | MCL-V | Train | website | 12 videos | | GOPRO | Train/Val | website | 33 videos, deblur | | CelebA | Train | website | Human faces | | Sintel | Train/Val | website | Optical flow | | FlyingChairs | Train | website | Optical flow | | Vimeo-90k | Train/Test | website | 90k HQ videos | | SR-RAW | Train/Test | website | raw sensor image dataset | | W2S | Train/Test | arxiv | A Joint Denoising and Super-Resolution Dataset | | PIPAL | Test | ECCV 2020 | Perceptual Image Quality Assessment dataset |
Dataset collections
Benckmark and DIV2K: Set5, Set14, B100, Urban100, Manga109, DIV2K2017 include bicubic downsamples with x2,3,4,8
SRtestingdatasets: Test: Set5, Set14, B100, Urban100, Manga109, Historical; Train: T91,General100, BSDS200
paper
Non-DL based approach
SCSR: TIP2010, Jianchao Yang et al.paper, code
ANR: ICCV2013, Radu Timofte et al. paper, code
A+: ACCV 2014, Radu Timofte et al. paper, code
IA: CVPR2016, Radu Timofte et al. paper
SelfExSR: CVPR2015, Jia-Bin Huang et al. paper, code
NBSRF: ICCV2015, Jordi Salvador et al. paper
RFL: ICCV2015, Samuel Schulter et al paper, code
DL based approach
Note this table is referenced from here
2014-2016
| Model | Published | Code | Keywords | | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | SRCNN | ECCV14 | Keras | Kaiming | | RAISR | arXiv | - | Google, Pixel 3 | | ESPCN | CVPR16 | Keras | Real time/SISR/VideoSR | | VDSR | CVPR16 | Matlab | Deep, Residual | | DRCN | CVPR16 | Matlab | Recurrent |
2017
| Model | Published | Code | Keywords | | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | DRRN | CVPR17 | Caffe, PyTorch | Recurrent | | LapSRN | CVPR17 | Matlab | Huber loss | | IRCNN | CVPR17 | Matlab | | | EDSR | CVPR17 | PyTorch | NTIRE17 Champion | | BTSRN | CVPR17 | - | NTIRE17 | | SelNet | CVPR17 | - | NTIRE17 | | TLSR | CVPR17 | - | NTIRE17 | | SRGAN | CVPR17 | Tensorflow | 1st proposed GAN | | VESPCN | CVPR17 | - | VideoSR | | MemNet | ICCV17 | Caffe | | | SRDenseNet | ICCV17 | -, PyTorch | Dense | | SPMC | ICCV17 | Tensorflow | VideoSR | | EnhanceNet | ICCV17 | TensorFlow | Perceptual Loss | | PRSR | ICCV17 | TensorFlow | an extension of PixelCNN | | AffGAN | ICLR17 | - | |
2018
| Model | Published | Code | Keywords | | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | MS-LapSRN | TPAMI18 | Matlab | Fast LapSRN | | DCSCN | arXiv | Tensorflow | | | IDN | CVPR18 | Caffe | Fast | | DSRN | CVPR18 | TensorFlow | Dual state，Recurrent | | RDN | CVPR18 | Torch | Deep, BI-BD-DN | | SRMD | CVPR18 | Matlab | Denoise/Deblur/SR | | xUnit | CVPR18 | PyTorch | Spatial Activation Function | | DBPN | CVPR18 | PyTorch | NTIRE18 Champion | | WDSR | CVPR18 | PyTorch，TensorFlow | NTIRE18 Champion | | ProSRN | CVPR18 | PyTorch | NTIRE18 | | ZSSR | CVPR18 | Tensorflow | Zero-shot | | FRVSR | CVPR18 | PDF | VideoSR | | DUF | CVPR18 | Tensorflow | VideoSR | | TDAN | arXiv | - | VideoSR，Deformable Align | | SFTGAN | CVPR18 | PyTorch | | | CARN | ECCV18 | PyTorch | Lightweight | | RCAN | ECCV18 | PyTorch | Deep, BI-BD-DN | | MSRN | ECCV18 | PyTorch | | | SRFeat | ECCV18 | Tensorflow | GAN | | TSRN | ECCV18 | Pytorch | | | ESRGAN | ECCV18 | PyTorch | PRIM18 region 3 Champion | | EPSR | ECCV18 | PyTorch | PRIM18 region 1 Champion | | PESR | ECCV18 | PyTorch | ECCV18 workshop | | FEQE | ECCV18 | Tensorflow | Fast | | NLRN | NIPS18 | Tensorflow | Non-local, Recurrent | | SRCliqueNet | NIPS18 | - | Wavelet | | CBDNet | arXiv | Matlab | Blind-denoise | | TecoGAN | arXiv | Tensorflow | VideoSR GAN |
2019
| Model | Published | Code | Keywords | | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | RBPN | CVPR19 | PyTorch | VideoSR | | SRFBN | CVPR19 | PyTorch | Feedback | | AdaFM | CVPR19 | PyTorch | Adaptive Feature Modification Layers | | MoreMNAS | arXiv | - | Lightweight，NAS | | FALSR | arXiv | TensorFlow | Lightweight，NAS | | Meta-SR | CVPR19 | PyTorch | Arbitrary Magnification | | AWSRN | arXiv | PyTorch | Lightweight | | OISR | CVPR19 | PyTorch | ODE-inspired Network | | DPSR | CVPR19 | PyTorch | | | DNI | CVPR19 | PyTorch | | | MAANet | arXiv | | Multi-view Aware Attention | | RNAN | ICLR19 | PyTorch | Residual Non-local Attention | | FSTRN | CVPR19 | - | VideoSR, fast spatio-temporal residual block | | MsDNN | arXiv | TensorFlow | NTIRE19 real SR 21th place | | SAN | CVPR19 | Pytorch | Second-order Attention,cvpr19 oral | | EDVR | CVPRW19 | Pytorch | Video, NTIRE19 video restoration and enhancement champions | | Ensemble for VSR | CVPRW19 | - | VideoSR, NTIRE19 video SR 2nd place | | TENet | arXiv | Pytorch | a Joint Solution for Demosaicking, Denoising and Super-Resolution | | MCAN | arXiv | Pytorch | Matrix-in-matrix CAN, Lightweight | | IKC&SFTMD | CVPR19 | - | Blind Super-Resolution | | SRNTT | CVPR19 | TensorFlow | Neural Texture Transfer | | RawSR | CVPR19 | TensorFlow | Real Scene Super-Resolution, Raw Images | | resLF | CVPR19 | | Light field | | CameraSR | CVPR19 | | realistic image SR | | ORDSR | TIP | model | DCT domain SR | | U-Net | CVPRW19 | | NTIRE19 real SR 2nd place, U-Net,MixUp,Synthesis | | DRLN | arxiv | | Densely Residual Laplacian Super-Resolution | | EDRN | CVPRW19 | Pytorch | NTIRE19 real SR 9th places | | FC2N | arXiv | | Fully Channel-Concatenated | | GMFN | BMVC2019 | Pytorch | Gated Multiple Feedback | | CNN&TV-TV Minimization | BMVC2019 | | TV-TV Minimization | | HRAN | arXiv | | Hybrid Residual Attention Network | | PPON | arXiv | code | Progressive Perception-Oriented Network | | SROBB | ICCV19 | | Targeted Perceptual Loss | | RankSRGAN | ICCV19 | PyTorch | oral, rank-content loss | | edge-informed | ICCVW19 | PyTorch | Edge-Informed Single Image Super-Resolution | | s-LWSR | arxiv | | Lightweight | | DNLN | arxiv | | Video SR Deformable Non-local Network | | MGAN | arxiv | | Multi-grained Attention Networks | | IMDN | ACM MM 2019 | PyTorch | AIM19 Champion | | ESRN | arxiv | | NAS | | PFNL | ICCV19 | Tensorflow | VideoSR oral,Non-Local Spatio-Temporal Correlations | | EBRN | ICCV19 | Tensorflow | Embedded Block Residual Network | | Deep SR-ITM | ICCV19 | matlab | SDR to HDR, 4K SR | | feature SR | ICCV19 | | Super-Resolution for Small Object Detection | | STFAN | ICCV19 | PyTorch | Video Deblurring | | KMSR | ICCV19 | PyTorch | GAN for blur-kernel estimation | | CFSNet | ICCV19 | PyTorch | Controllable Feature | | FSRnet | ICCV19 | | Multi-bin Trainable Linear Units | | SAM+VAM | ICCVW19 | | | | SinGAN | ICCV19 | PyTorch | bestpaper, train from single image |
2020
| Model | Published | Code | Keywords | | ---------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | FISR | AAAI 2020 | TensorFlow | Video joint VFI-SR method,Multi-scale Temporal Loss | | ADCSR | arxiv | | | | SCN | AAAI 2020 | | Scale-wise Convolution | | LSRGAN | arxiv | | Latent Space Regularization for srgan | | Zooming Slow-Mo | CVPR 2020 | PyTorch | joint VFI and SR，one-stage， deformable ConvLSTM | | MZSR | CVPR 2020 | | Meta-Transfer Learning, Zero-Shot | | VESR-Net | arxiv | | Youku Video Enhancement and Super-Resolution Challenge Champion | | blindvsr | arxiv | PyTorch | Motion blur estimation | | HNAS-SR | arxiv | PyTorch | Hierarchical Neural Architecture Search, Lightweight | | DRN | CVPR 2020 | PyTorch | Dual Regression, SISR STOA | | SFM | arxiv | PyTorch | Stochastic Frequency Masking, Improve method | | EventSR | CVPR 2020 | | split three phases | | USRNet | CVPR 2020 | PyTorch | | | PULSE | CVPR 2020 | | Self-Supervised | | SPSR | CVPR 2020 | Code | Gradient Guidance, GAN | | DASR | arxiv | Code | Real-World Image Super-Resolution, Unsupervised SuperResolution, Domain Adaptation. | | STVUN | arxiv | PyTorch | Video Super-Resolution, Video Frame Interpolation, Joint space-time upsampling | | AdaDSR | arxiv | PyTorch | Adaptive Inference | | Scale-Arbitrary SR | arxiv | Code | Scale-Arbitrary Super-Resolution, Knowledge Transfer | | DeepSEE | arxiv | Code | Extreme super-resolution,32× magnification | | CutBlur | CVPR 2020 | PyTorch | SR Data Augmentation | | UDVD | CVPR 2020 | | Unified Dynamic Convolutional，SISR and denoise | | DIN | IJCAI-PRICAI 2020 | | SISR，asymmetric co-attention | | PANet | arxiv |PyTorch | Pyramid Attention | | SRResCGAN | arxiv |PyTorch | | | ISRN | arxiv | | iterative optimization, feature normalization. | | RFB-ESRGAN | CVPR 2020 | | NTIRE 2020 Perceptual Extreme Super-Resolution Challenge winner | | PHYSICSSR | AAAI 2020 | PyTorch | | | CSNLN | CVPR 2020 | PyTorch | Cross-Scale Non-Local Attention,Exhaustive Self-Exemplars Mining, Similar to PANet | | TTSR | CVPR 2020 | PyTorch | Texture Transformer | | NSR | arxiv | PyTorch | Neural Sparse Representation | | RFANet | CVPR 2020 | | state-of-the-art SISR | | Correction filter | CVPR 2020 | | Enhance SISR model generalization | | Unpaired SR | CVPR 2020 | |Unpaired Image Super-Resolution | | STARnet | CVPR 2020 | |Space-Time-Aware multi-Resolution | | SSSR | CVPR 2020 | code |SISR for Semantic Segmentation and Human pose estimation | | VSRTGA | CVPR 2020 | code | Temporal Group Attention, Fast Spatial Alignment | | SSEN | CVPR 2020 | | Similarity-Aware Deformable Convolution | | SMSR | arxiv | | Sparse Masks, Efficient SISR | LF-InterNet | ECCV 2020 | PyTorch | Spatial-Angular Interaction, Light Field Image SR | | Invertible-Image-Rescaling | ECCV 2020 | Code | ECCV oral | | IGNN | arxiv | Code | GNN, SISR | | MIRNet | ECCV 2020 | PyTorch | multi-scale residual block | | SFM | ECCV 2020 | PyTorch | stochastic frequency mask | | TCSVT | arxiv | TensorFlow | LightWeight modules | | PISR | ECCV 2020 | PyTorch | FSRCNN,distillation framework, HR privileged information | | MuCAN | ECCV 2020 | | VideoSR, Temporal Multi-Correspondence Aggregation | | DGP | ECCV 2020 |PyTorch | ECCV oral, GAN, Image Restoration and Manipulation, | | RSDN| ECCV 2020 |Code | VideoSR, Recurrent Neural Network, TwoStream Block| | CDC| ECCV 2020 |PyTorch | Diverse Real-world SR dataset, Component Divide-and-Conquer model, GradientWeighted loss| | MS3-Conv| arxiv | | Multi-Scale cross-Scale Share-weights convolution | | OverNet| arxiv | | Lightweight, Overscaling Module, multi-scale loss, Arbitrary Scale Factors | | RRN| BMVC20 | code | VideoSR, Recurrent Residual Network, temporal modeling method | | NAS-DIP| ECCV 2020 | | NAS| | SRFlow| ECCV 2020 |code | Spotlight, Normalizing Flow| | LatticeNet| ECCV 2020 | |Lattice Block, LatticeNet, Lightweight, Attention| | BSRN| ECCV 2020 | |Model Quantization, Binary Neural Network, Bit-Accumulation Mechanism| | VarSR| ECCV 2020 | |Variational Super-Resolution, very low resolution | | HAN| ECCV 2020 | |SISR, holistic attention network, channel-spatial attention module | | DeepTemporalSR| ECCV 2020 | |Temporal Super-Resolution | | DGDML-SR| ECCV 2020 | |Zero-Shot, Depth Guided Internal Degradation Learning | |MLSR| ECCV 2020 | |Meta-learning, Patch recurrence | |PlugNet| ECCV 2020 | |Scene Text Recognition, Feature Squeeze Module | |TextZoom| ECCV 2020 |code |Scene Text Recognition | |TPSR| ECCV 2020 | |NAS,Tiny Perceptual SR | |CUCaNet| ECCV 2020 | PyTorch |Coupled unmixing, cross-attention,hyperspectral super-resolution, multispectral, unsupervised | |MAFFSRN| ECCVW 2020 | |Multi-Attentive Feature Fusion, Ultra Lightweight | |SRResCycGAN| ECCVW 2020 | PyTorch |RealSR, CycGAN | |A-CubeNet| arxiv | |SISR, lightweight| |MoG-DUN| arxiv | |SISR | |Understanding Deformable Alignment| arxiv | | VideoSR, EDVR, offset-fidelity loss | |AdderSR| arxiv | | SISR, adder neural networks, Energy Efficient | |RFDN| arxiv | | SISR, Lightweight, IMDN, AIM20 WINNER | |Tarsier| arxiv | | improve NESRGAN+,injected noise, Diagonal CMA optimize | |DeFiAN| arxiv | PyTorch |SISR, detail-fidelity attention, Hessian filtering |
Super Resolution workshop papers
NTIRE17 papers
NTIRE18 papers
PIRM18 Web
NTIRE19 papers
AIM19 papers
NTIRE20 papers
AIM20 Web
Super Resolution survey
[1] Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue. Deep Learning for Single Image Super-Resolution: A Brief Review. arxiv, 2018. paper
[2]Saeed Anwar, Salman Khan, Nick Barnes. A Deep Journey into Super-resolution: A survey. arxiv, 2019.paper
[3]Wang, Z., Chen, J., & Hoi, S. C. (2019). Deep learning for image super-resolution: A survey. arXiv preprint arXiv:1902.06068.paper
[4]Hongying Liu and Zhubo Ruan and Peng Zhao and Fanhua Shang and Linlin Yang and Yuanyuan Liu. Video Super Resolution Based on Deep Learning: A comprehensive survey. arXiv preprint arXiv:2007.12928.paper