awesome-image-super-resolution
Paper list and implementation (training and testing codes & results) in an unified project of CNN-based single image super-resolution algorithms, inspired by Single-Image-Super-Resolution.
All the implementation results are compared with reported results in their papers. For implementation details (training and testing codes), please refer to [wiki].
Everyone is free to use the results for qualitative comparisons in your own work, I hope this repository can help you a lot! If you have any suggestions, please kindly send an e-mail to yuan.ma@whut.edu.cn.
Survey paper
[1] Anwar S, Khan S, Barnes N. A deep journey into super-resolution: A survey[J]. arXiv preprint arXiv:1904.07523, 2019. [paper]
Paper list and implementation results of PSNR-maximization algorithms
[1] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. TPAMI, 2015, 38(2): 295-307.
| algorithms & PSNR/SSIM | Set5 | Set14 | B100 | Urban100 | Manga109 |
|---|---|---|---|---|---|
| SRCNN[paper] | 30.48/0.8628 | 27.50/0.7513 | 26.90/0.7103 | 24.52/0.7226 | 27.66/0.8580 |
| implementation[[results]](to be filled) | to be filled | to be filled | to be filled | to be filled | to be filled |
Paper list and implementation results of PI-minimization algorithms
[1] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//CVPR. 2017: 4681-4690.
| algorithms & PI/RMSE | Set5 | Set14 | B100 | Urban100 | Manga109 | PIRM-SR |
|---|---|---|---|---|---|---|
| SRGAN[paper] | to be filled | to be filled | to be filled | to be filled | to be filled | to be filled |
| implementation[[results]](to be filled) | to be filled | to be filled | to be filled | to be filled | to be filled | to be filled |