【资源】超分辨率相关资源大列表

作者:ChaofWang
来源:ChaofWang/Awesome-Super-Resolution

分享一个超分辨率相关资源列表,含图像和视频超分辨,内容包括相关论文、数据集、资料库等。


Awesome-Super-Resolution(in progress)

repositories

Awesome paper list:

Single-Image-Super-Resolution

Super-Resolution.Benckmark

Video-Super-Resolution

VideoSuperResolution

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


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
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


Dataset collections

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


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

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 PyTorchTensorFlow 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
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


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





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