DRCN http://www.drcn.org/ The International Workshop on Design of Reliable Communication Networks (DRCN)
2016年10月转: image super-resolution分类_DavFrank_新浪博客 http://blog.sina.com.cn/s/blog_82a927880102wbpx.html
查DRCN时逛到的一个帖子，对最近的超分辨率问题整理得很全。
转了一部分，但是从这里最后的Quantitative comparisons可以看出，如果单从传统学习的角度，对于PSNR的提升，可能已经很难超越DL。当然有一些基于重建的方法可能会接近，但是时间消耗太大，只能学术玩玩。
其实不只是超分辨率问题，涉及到CV的各个方面，很多最近几年都被DL打压的很厉害，是不是会出现DL一统江湖的情况？我想很难，最后的一个结果会是什么样让人期待。
github：https://github.com/huangzehao/Super-Resolution.Benckmark
1---Classical Sparse Coding Method
① ScSR [Web]
- Image super-resolution as sparse representation of raw image patches (CVPR2008), Jianchao Yang et al.
- Image super-resolution via sparse representation (TIP2010), Jianchao Yang et al.
- Coupled dictionary training for image super-resolution (TIP2011), Jianchao Yang et al.
杨建超-基于稀疏表达的图像超分辨重建-学习札记 - 简书 https://www.jianshu.com/p/96d968066b78?utm_campaign
2---Anchored Neighborhood Regression Method
- ① ANR [Web]
- Anchored Neighborhood Regression for Fast Example-Based Super-Resolution (ICCV2013), Radu Timofte et al.
- ② A+ [Web]
- A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution (ACCV2014), Radu Timofte et al.
- ③ IA [Web]
- Seven ways to improve example-based single image super resolution (CVPR2016), Radu Timofte et al.
Self-Exemplars
- 3---SelfExSR [Web]
① Single Image Super-Resolution from Transformed Self-Exemplars (CVPR2015), Jia-Bin Huang et al.
4---Bayes
- ① NBSRF [Web]
- Naive Bayes Super-Resolution Forest (ICCV2015), Jordi Salvador et al.
5---Deep Learning Method
- ①SRCNN [Web]
- Image Super-Resolution Using Deep Convolutional Networks (ECCV2014), Chao Dong et al.
- Image Super-Resolution Using Deep Convolutional Networks (TPAMI2015), Chao Dong et al.
- ② CSCN [Web]
- Deep Networks for Image Super-Resolution with Sparse Prior (ICCV2015), Zhaowen Wang et al.
- Robust Single Image Super-Resolution via Deep Networks with Sparse Prior (TIP2016), Ding Liu et al.
- ③ VDSR [Web]
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR2016), Jiwon Kim et al.
- ④ DRCN [Web]
- Deeply-Recursive Convolutional Network for Image Super-Resolution (CVPR2016), Jiwon Kim et al.
- ⑤ ESPCN [PDF]
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR2016), Wenzhe Shi et al.
- Is the deconvolution layer the same as a convolutional layer? [PDF]
- ⑥ FSRCNN [Web]
- Acclerating the Super-Resolution Convolutional Neural Network (ECCV2016), Dong Chao et al.
6---Perceptual Loss
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution (ECCV2016), Justin Johnson et al.
- SRGAN [PDF]
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, Christian Ledig et al.