Awesome
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Overview
An unofficial implementation of SRGAN described in the paper using PyTorch.
Published in CVPR 2017
Requirement
- Python 3.6.5
- PyTorch 1.1.0
- Pillow 5.1.0
- numpy 1.14.5
- scikit-image 0.15.0
Datasets
Pre-trained model
Train & Test
Train
python main.py --LR_path ./LR_imgs_dir --GT_path ./GT_imgs_dir
Test
python main.py --mode test --LR_path ./LR_imgs_dir --GT_path ./GT_imgs_dir --generator_path ./model/SRGAN.pt
Inference your own images
python main.py --mode test_only --LR_path ./LR_imgs_dir --generator_path ./model/SRGAN.pt
Experimental Results
Experimental results on benchmarks.
Quantitative Results
| Method | Set5 | Set14 | B100 |
|---|---|---|---|
| Bicubic | 28.43 | 25.99 | 25.94 |
| SRResNet(paper) | 32.05 | 28.49 | 27.58 |
| SRResNet(my model) | 31.96 | 28.48 | 27.49 |
| SRGAN(paper) | 29.40 | 26.02 | 25.16 |
| SRGAN(my model) | 29.93 | 26.95 | 26.10 |
Qualitative Results
| Bicubic | SRResNet | SRGAN |
|---|---|---|
| <img src="result/Set14_BIx4/comic_LRBI_x4.png"> | <img src="result/set14_srres_result/res_0004.png"> | <img src="result/set14_srgan_result/res_0004.png"> |
| <img src="result/Set5_BIx4/woman_LRBI_x4.png"> | <img src="result/set5_srres_result/res_0004.png"> | <img src="result/set5_srgan_result/res_0004.png"> |
| <img src="result/Set14_BIx4/baboon_LRBI_x4.png"> | <img src="result/set14_srres_result/res_0000.png"> | <img src="result/set14_srgan_result/res_0000.png"> |
Comments
If you have any questions or comments on my codes, please email to me. son1113@snu.ac.kr