方法论文

EDSR(enhanced deep super-resolution network)

MDSR(multi-scaledeep super-resolution system)

Enhanced Deep Residual Networks for Single Image Super-Resolution cvprw2017

https://github.com/LimBee/NTIRE2017

srresnet的网络结构简化版

VDSR

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/Jongchan/tensorflow-vdsr

https://github.com/twtygqyy/pytorch-vdsr

可能都不是官方的git

A+

RFL

SelfEx

SRCNN

Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 2016

http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html

DRRN（Deep Recursive Residual Network）

Image Super-Resolution via Deep RecursiveResidual NetworkCVPR2017

https://github.com/tyshiwo/DRRN_CVPR17

MemNet

MemNet: A Persistent Memory Network for Image RestorationICCV2017

DRCN

Deeply-recursive convolutional network for image super-resolution. In CVPR, 2016.

RED-Net   RED10   RED20   RED30

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections  NIPS2016

compared with SRCNN [7], NBSRF [25], CSCN [30], CSC [10], TSE [13] and ARFL+ [26]

NBSRF

Naive bayes super-resolution forest. In Proc. IEEE Int. Conf. Comp. Vis.2015

https://perezpellitero.github.io/

IRCNN

Learning Deep CNN Denoiser Prior for Image Restoration(CVPR2017)

https://github.com/cszn/ircnn

CSCN(cascade of sparse coding based network)

Deep networks for image super-resolution with sparse prior. In Proc. ICCV 2015.

http://www.ifp.illinois.edu/~dingliu2/iccv15/

CSC(Convolutional Sparse Coding)

Convolutional Sparse Coding for Image Super-resolution ICCV2015

TSE(Transformed Self-Exemplars)

Single Image Super-resolution from Transformed Self-Exemplars  CVPR2015

ARFL+

Fast and Accurate Image Upscaling with Super-Resolution ForestsCVPR2015