超分辨率相关资源大列表-1

本文收藏自Github，仅供学习交流所用。

 

Awesome-Super-Resolution（in progress）

Collect some super-resolution related papers, data and repositories.

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	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
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
EDVR	CVPR19	Pytorch	Video, NTIRE19 video restoration and enhancement champions
Ensemble for VSR	CVPR19	-	VideoSR, NTIRE19 video SR 2nd place
TENet	arXiv	-	a Joint Solution for Demosaicking, Denoising and Super-Resolution
MCAN	arXiv	Pytorch	Matrix-in-matrix CAN, Lightweight
IKC&SFTMD	CVPR19	-	Blind Super-Resolution

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