- repositories
- Datasets
- paper
- Super Resolution workshop papers
- NTIRE17
- NTIRE18
- PIRM18
- NTIRE19
- AIM19
- Super Resolution survey
Collect some super-resolution related papers, data and repositories.
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 |
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 |
Benckmark and DIV2K: Set5, Set14, B100, Urban100, Manga109, DIV2K2017 include bicubic downsamples with x2,3,4,8
SR_testing_datasets: Test: Set5, Set14, B100, Urban100, Manga109, Historical; Train: T91,General100, BSDS200
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
Note this table is referenced from here
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 |
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 | - |
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 | 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 |
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 |
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 | |
PHYSICS_SR | 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 |
VSR_TGA | 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 |
NTIRE17 papers
NTIRE18 papers
PIRM18 Web
NTIRE19 papers
AIM19 papers
NTIRE20 papers
NOTE! AIM20 Started!
[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