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# ESRGAN (Enhanced SRGAN) [[Paper]](https://arxiv.org/abs/1809.00219) [[BasicSR]](https://github.com/xinntao/BasicSR)
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## Enhanced Super-Resolution Generative Adversarial Networks
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### Enhanced Super-Resolution Generative Adversarial Networks
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By Xintao Wang, [Ke Yu](https://yuke93.github.io/), Shixiang Wu, [Jinjin Gu](http://www.jasongt.com/), Yihao Liu, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Xiaoou Tang](https://scholar.google.com/citations?user=qpBtpGsAAAAJ&hl=en)
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This repo only provides simple testing codes, pretrained models and the netwrok strategy demo.
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- Python 3
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- PyTorch >= 0.4.0
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- Python package `cv2`, `numpy`
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#### Test
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### Test models
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1. Clone this github repo.
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```
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git clone https://github.com/xinntao/ESRGAN
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python test.py models/RRDB_PSNR_x4.pth
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```
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5. The results are in `./results` folder.
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### Network interpolation demo
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We can interpolate the RRDB_ESRGAN and RRDB_PSNR models with alpha in [0, 1].
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1. Run `python net_interp.py 0.8`, where *0.8* is the interpolation parameter.
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2. Run `python test.py models/interp_08.pth`, where *models/interp_08.pth* is the model path.
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<p align="center">
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<img height="400" src="figures/43074.gif">
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</p>
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## Introduction
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We improve the [SRGAN](https://arxiv.org/abs/1609.04802) from three aspects:
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</p>
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We show the smooth animation with the interpolation parameters changing from 0 to 1.
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<p align="center">
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<img height="400" src="figures/43074.gif">
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</p>
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<p align="center">
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<img height="480" src="figures/81.gif">
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