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 |
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 |
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 |
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 |
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 | - | |
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 |
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 |
ESRGAN | ECCV18 | PyTorch | PRIM18 region 3 Champion |
FEQE | ECCV18 | Tensorflow | Fast |
NLRN | NIPS18 | Tensorflow | Non-local, Recurrent |
SRCliqueNet | NIPS18 | - | Wavelet |
CBDNet | arXiv | Matlab | Blind-denoise |
TecoGAN | arXiv | Tensorflow | VideoSR GAN |
RBPN | CVPR19 | PyTorch | VideoSR |
SRFBN | CVPR19 | PyTorch | Feedback |
MoreMNAS | arXiv | - | Lightweight,NAS |
FALSR | arXiv | TensorFlow | Lightweight,NAS |
Meta-SR | arXiv | 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 |
[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