Collect some image SR related papers, datasets, metrics and repositories.
Most of these contents are referenced from here. Thank you!!!
Paper with code: Super Resolution
repo | Framework |
---|---|
EDSR-PyTorch | PyTorch |
RCAN-PyTorch | PyTorch |
CARN-PyTorch | PyTorch |
BasicSR | PyTorch |
Image-Super-Resolution | Keras |
image-super-resolution | Keras |
Super-Resolution-Zoo | MxNet |
super-resolution | Keras |
neural-enhance | Theano |
srez | Tensorflow |
waifu2x | Torch |
Super-resolution | PyTorch |
VideoSuperResolution | Tensorflow |
Video-super-resolution | PyTorch |
Note this table is referenced from here.
Metric | Papers |
---|---|
MS-SSIM | Multiscale structural similarity for image quality assessment, Wang, Zhou; Simoncelli, Eero P.; Bovik, Alan C., ACSSC 2003, [ACSSC], MS-SSIM |
SSIM | Image Quality Assessment: From Error Visibility to Structural Similarity, Wang, Zhou; Bovik, Alan C.; Sheikh, Hamid R.; Simoncelli, Eero P, TIP 2004, [TIP], SSIM |
IFC | An information fidelity criterion for image quality assessment using natural scene statistics, Sheikh, Hamid Rahim; Bovik, Alan Conrad; de Veciana, Gustavo de Veciana, TIP 2005, [TIP], IFC |
VIF | Image information and visual quality, Sheikh, Hamid Rahim; Bovik, Alan C., TIP 2006, [TIP], VIF |
FSIM | FSIM: A Feature Similarity Index for Image Quality Assessment, Zhang, Lin; Zhang, Lei; Mou, Xuanqin; Zhang, David, TIP 2011, [Project], [TIP], FSIM |
NIQE | Making a “Completely Blind” Image Quality Analyzer, Mittal, Anish; Soundararajan, Rajiv; Bovik, Alan C., Signal Processing Letters 2013, [Matlab*], [Signal Processing Letters], NIQE |
Ma | Learning a no-reference quality metric for single-image super-resolution, Ma, Chao; Yang, Chih-Yuan; Yang, Xiaokang; Yang, Ming-Hsuan, CVIU 2017, [arXiv], [CVIU], [Matlab*], [Project], Ma |
Note this table is referenced from here.
Name | Usage | Link | Comments |
---|---|---|---|
Set5 | Test | download | super-resolution test dataset |
SET14 | Test | download | super-resolution test dataset |
BSD100 | Test | download | super-resolution test dataset |
Urban100 | Test | download | super-resolution test dataset |
Manga109 | Test | download | super-resolution test dataset |
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 |
SR-RAW | Train/Test | website | raw sensor image dataset |
Benchmark and DIV2k(SR) | Train/Test | website | super-resolution dataset |
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
Model | Published | Code | Keywords |
---|---|---|---|
SRCNN | ECCV14 | Keras | CNN |
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 | Dense && Recurrent |
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 && Dense |
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 && Progressive |
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 && Channel-wise Attention |
MSRN | ECCV18 | PyTorch | Multi-scale |
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 |
Model | Published | Code | Keywords |
---|---|---|---|
CBDNet | arXiv | Matlab | Blind-denoise |
TecoGAN | arXiv | Tensorflow | VideoSR GAN |
RBPN | CVPR19 | PyTorch | VideoSR |
SRFBN | CVPR19 | PyTorch | Feedback && Recurrent |
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 |
s-LWSR | arXiv | Lightweight Network | |
ESRN | AAAI2020 | ||
MGAN | arXiv | ||
IMDN | ACM MM 2019 | PyTorch | Lightweight Network |
WDST | arXiv | perception-distortion tradeoff | |
HBPN | arXiv | PyTorch | |
ABPN | arXiv | PyTorch | |
PFNL | ICCV19 | Tensorflow | VideoSR oral,Non-Local Spatio-Temporal Correlations |
EBRN | ICCV19 | 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 |
JDSR | arXiv | Demosaicing and SR | |
CFSNet | ICCV19 | PyTorch | Controllable Feature |
FSRnet | ICCV19 | Multi-bin Trainable Linear Units | |
SAM+VAM | ICCVW19 |
Model | Published | Code | Keywords |
---|---|---|---|
FFA-Net | AAAI2020 | Pytorch | Image Dehazing |
RC-Net | arXiv | matlab | Image Denoising and Super-resolution |
IR-NAS | arXiv | NAS | |
SISR-CA-OA | arXiv | Fast &Channel-Attention | |
DSGAN | arXiv | Real-World Super-Resolution | |
ADCSR | arXiv | ||
SCN | AAAI2020 | Scale-wise Convolution | |
MLSR | arXiv | Self-supervised | |
GAN-based | arXiv | Real-world SR | |
ESRGAN+ | arXiv | ||
SOF-VSR | TIP ACCV | Pytorch | Video SR |
[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
NTIRE17 papers
NTIRE18 papers
PIRM18 web
NTIRE19 papers
AIM19 papers