(1)稀疏編碼方法(Sparse Coding)
- Image super-resolution as sparse representation of raw image patches (CVPR2008)
- 楊建超主頁:http://www.ifp.illinois.edu/~jyang29/
- 基於原始圖像塊稀疏表示的圖像超分辨率
- Image super-resolution via sparse representation (TIP2010)
- Coupled dictionary training for image super-resolution (TIP2011)
(2)Self-Exemplars
- Single Image Super-Resolution from Transformed Self-Exemplars (CVPR2015)
- Jia-Bin Huang主頁:https://sites.google.com/site/jbhuang0604/
(3)貝葉斯方法
- NBSRF:https://jordisalvador-image.blogspot.com/2015/08/iccv-2015.html
- Naive Bayes Super-Resolution Forest (ICCV2015)
(4)基於金字塔算法
(5)深度學習方法(近幾年文章很多啊)
- Image Super-Resolution Using Deep Convolutional Networks (ECCV2014)
- Deep Networks for Image Super-Resolution with Sparse Prior (ICCV2015)
- Robust Single Image Super-Resolution via Deep Networks with Sparse Prior (TIP2016)
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR2016)
- Deeply-Recursive Convolutional Network for Image Super-Resolution (CVPR2016)
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR2016)
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (CVPR 2017),
- Enhanced Deep Residual Networks for Single Image Super-Resolution (Winner of NTIRE2017 Super-Resolution Challenge)
關於深度學習在超分辨率重建中的應用:https://zhuanlan.zhihu.com/p/25532538?utm_medium=social&utm_source=weibo
給出了幾種實現方法及介紹,github裏面相應的項目實現。另外還發現一篇有點尺度的文章《用GAN去除(愛情)動作片中的馬賽克和衣服》,感興趣的請參見這裏。
(6)Perceptual Loss and GAN(損失函數上改進)
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution (ECCV2016)
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (CVPR2017)
(7)Google基於哈希機制的實現
分析:http://blog.csdn.net/jiangjieqazwsx/article/details/69055753
(8)視頻SR
- https://users.soe.ucsc.edu/~milanfar/software/superresolution.html
- Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (CVPR2017)
小結:SR使用稀疏編碼方法取得的方法已經堪稱state-of-the-art級別,深度學習出現後又將效果進一步提升。
增補:
今天看到一篇論文:
《Super-Resolution From a Single Image 》(http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html),
http://cs.brown.edu/courses/csci1950-g/results/final/pachecoj/ ,
另外附幾個相關網頁:
https://people.mpi-inf.mpg.de/~kkim/supres/supres.htm
《Example-Based-Super-Resolution-Freeman》
增補:
神經網絡實現:
(1)《Accelerating the Super-Resolution Convolutional Neural Network》,使用matlab的實現。
(2)《Pixel Recursive Super Resolution》,項目實現鏈接。
180911增補:
有關項目網站:https://github.com/huangzehao/Super-Resolution.Benckmark