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Tensorflow implementation of the SRGAN algorithm for single image super-resolution
| Date | Stars |
|---|---|
| 2026-07-24 | 857 |
| 2026-07-25 | 857 |
| 2026-07-28 | 857 |
| 2026-07-30 | 857 |
| 2026-08-06 | 857 |
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# SRGAN-tensorflow
### Introduction
This project is a tensorflow implementation of the impressive work [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/pdf/1609.04802.pdf). <br />
The result is obtained following to same setting from the v5 edition of the [paper on arxiv](https://arxiv.org/pdf/1609.04802.pdf). However, due to limited resources, I train my network on the [RAISE dataset](http://mmlab.science.unitn.it/RAISE/) which contains 8156 high resoution images captured by good cameras. As the results showed below, the performance is close to the result presented in the paper without using the imagenet training set. <br />
The result on BSD100, Set14, Set5 will be reported later. The code is highly inspired by the [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow).
#### Some results:
* The comparison of some result form my implementation and the paper
<table >
<tr >
<td><center>Inputs</center></td>
<td><center>Our result</center></td>
<td><center>SRGAN result</center></td>
<td><center>Original</center></td>
</tr>
<tr>
<td>
<center><img src="./pic/SRGAN/comic_LR.png" height="280"></center>
</td>
<td>
<center><img src="./pic/images/img_005-outputs.png" height="280"></center>
</td>
<td>
<center><img src="./pic/SRGAN/comic_SRGAN-VGG54.png" height="280"></center>
</td>
<td>
<center><img src="./pic/SRGAN/comic_HR.png" height="280"></center>
</td>
</tr>
<tr>
<td><center>Inputs</center></td>
<td><center>Our result</center></td>
<td><center>SRGAN result</center></td>
<td><center>Original</center></td>
</tr>
<tr>
<td>
<center><img src="./pic/SRGAN/baboon_LR.png" height="200"></center>
</td>
<td>
<center><img src="./pic/images/img_001-outputs.png" height="200"></center>
</td>
<td>
<center><img src="./pic/SRGAN/baboon_SRGAN-VGG54.png" height="200"></center>
</td>
<td>
<center><img src="./pic/images/img_001-targets.png" height="200"></center>
</td>
</tr>
</table>
### Denpendency
* python2.7
* tensorflow (tested on r1.0, r1.2)
* Download and extract the pre-trained model from my [google drive](https://drive.google.com/a/gapp.nthu.edu.tw/uc?id=0BxRIhBA0x8lHNDJFVjJEQnZtcmc&export=download)
* Download the VGG19 weights from the [TF-slim models](http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz)
* The code is tested on:
* Ubuntu 14.04 LTS with CPU architecture x86_64 + Nvidia Titan X
* Ubuntu 16.04 LTS with CPU architecture x86_64 + Nvidia 1080, 1080Ti or Titan X
### Recommended
* Ubuntu 16.04 with tensorflow GPU edition
### Getting Started
Throughout the project, we denote the directory you cloned the repo as ```SRGAN-tensorflow_ROOT```<br />
* #### Run test using pre-trained model
```bash
# clone the repository from github
git clone https://github.com/brade31919/SRGAN-tensorflow.git
cd $SRGAN-tensorflow_ROOT/
# Download the pre-trained model from the google-drive
# Go to https://drive.google.com/a/gapp.nthu.edu.tw/uc?id=0BxRIhBA0x8lHNDJFVjJEQnZtcmc&export=download
# Download the pre-trained model to SRGAN-tensorflow/
tar xvf SRGAN_pre-trained.tar
# Run the test mode
sh test_SRGAN.sh
#The result can be viewed at $SRGAN-tensorflow_ROOT/result/images/
```
<br />
* #### Run the inference using pre-trained model on your own image
```bash
cd $SRGAN-tensorflow_ROOT/
# Download the pre-trained model from the google-drive
# Go to https://drive.google.com/a/gapp.nthu.edu.tw/uc?id=0BxRIhBA0x8lHNDJFVjJEQnZtcmc&export=download
# Download the pre-trained model to SRGAN-tensorflow/
tar xvf SRGAN_pre-trained.tar
# put your png images in the your own directory
# For example
mkdir myImages
# put some images in it
```
modify the path in inference_SRGAN.sh
```bash
#!/usr/bin/env bash
CUDA_VISIBLE_DExcerpt of 13,112 characters
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Alexander Wu · United States
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youcai · United Kingdom
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0f86b145cc154e7d, topic:deep-learning, topic:tensorflow
matched fp:0f86b145cc154e7d, topic:super-resolution, desc:super resolution, readme:super resolution