68 lines
2 KiB
Markdown
68 lines
2 KiB
Markdown
# Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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## Overview
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An unofficial implementation of SRGAN described in the paper using PyTorch.
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* [ Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802)
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Published in CVPR 2017
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## Requirement
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- Python 3.6.5
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- PyTorch 1.1.0
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- Pillow 5.1.0
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- numpy 1.14.5
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- scikit-image 0.15.0
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## Datasets
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- [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/)
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## Pre-trained model
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- [SRResNet](https://drive.google.com/open?id=15F2zOrOg2hIjdI0WsrOwF1y8REOkmmm0)
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- [SRGAN](https://drive.google.com/open?id=1-HmcV5X94u411HRa-KEMcGhAO1OXAjAc)
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## Train & Test
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Train
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```
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python main.py --LR_path ./LR_imgs_dir --GT_path ./GT_imgs_dir
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```
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Test
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```
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python main.py --mode test --LR_path ./LR_imgs_dir --GT_path ./GT_imgs_dir --generator_path ./model/SRGAN.pt
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```
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Inference your own images
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```
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python main.py --mode test_only --LR_path ./LR_imgs_dir --generator_path ./model/SRGAN.pt
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```
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## Experimental Results
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Experimental results on benchmarks.
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### Quantitative Results
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| Method| Set5| Set14| B100 |
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|-------|-----|------|------|
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|Bicubic|28.43|25.99|25.94|
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|SRResNet(paper)|32.05|28.49|27.58|
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|SRResNet(my model)|31.96|28.48|27.49|
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|SRGAN(paper)|29.40|26.02|25.16|
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|SRGAN(my model)|29.93|26.95|26.10|
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### Qualitative Results
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| Bicubic | SRResNet | SRGAN |
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| --- | --- | --- |
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| <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"> |
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| <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"> |
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| <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"> |
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## Comments
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If you have any questions or comments on my codes, please email to me. [son1113@snu.ac.kr](mailto:son1113@snu.ac.kr)
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