1、Second-order Attention Network for Single Image Super-Resolution
基于注意力网络的改进，论文客观指标最高
http://www4.comp.polyu.edu.hk/~cslzhang/paper/CVPR19-SAN.pdf
2、Image Super-Resolution by Neural Texture Transfer
比较填充的手段，思路很好
https://arxiv.org/pdf/1903.00834.pdf
3、Feedback Network for Image Super-Resolution
新思路，效果顶尖，值得深入
https://github.com/Paper99/SRFBN_CVPR19
4、Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
能应对不同下采样方式。。。感觉很水
https://github.com/cszn/DPSR
5、Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
任意比列超分
http://www.mingriqingbao.com/web/detail/forword/P/36079
6、Deep Back-Projection Networks for Super-Resolution
cvpr2018 ，18年赢了很多奖项
https://github.com/alterzero/DBPN-Pytorch
7、Recurrent Back-Projection Network for Video Super-Resolution
基于上面单张图像做的改进，应用于视频超分辨率
https://github.com/alterzero/RBPN-PyTorch
一下论文资源尚未公布
8、Residual Networks for Light Field Image Super-Resolution
9、Hyperspectral Image Super-Resolution With Optimized RGB Guidance
10、ODE-Inspired Network Design for Single Image Super-Resolution
11、Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination
12、Learning Parallax Attention for Stereo Image Super-Resolution
13、Fast Spatio-Temporal Residual Network for Video Super-Resolution
14、3D Appearance Super-Resolution With Deep Learning
15、Blind Super-Resolution With Iterative Kernel Correction
16、Towards Real Scene Super-Resolution With Raw Images