SR领域文献

标签：repr   提升   ant   cvpr   oss   rsa   ted   传统   neu   

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

• 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.

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]

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.

SR领域文献

标签：repr   提升   ant   cvpr   oss   rsa   ted   传统   neu   

原文地址：http://www.cnblogs.com/wxl845235800/p/7705298.html