# Scale-Iterative Upscaling Network for Image Deblurring
by Minyuan Ye, Dong Lyu and Gengsheng Chen<br>
pdf [[main](https://ieeexplore.ieee.org/document/8963625)][[backup](http://lab.zhuzhuguowang.cn:36900/croxline/Paper/Scale-Iterative%20Upscaling%20Network%20for%20Image%20Deblurring.pdf)]
### One real example
![/comparisions/images_in_paper/real_building1_comparision.png](../master/comparisons/images_in_paper/Real_building1_comparison.png)<br>
(a) Result of Nah et al. (b) Result of Tao et al. (c) Result of Zhang et al. (d) Our result.
<br>
### Results on benchmark datasets
![/comparisions/images_in_paper/benchmark_comparison.png](../master/comparisons/images_in_paper/benchmark_comparison.png)<br>
From top to bottom are blurry input, deblurring results of Nah et al., Tao et al., Zhang et al. and ours.<br>
<br>
### Results on real-world blurred images
![/comparisions/images_in_paper/real_comparison.png](../master/comparisons/images_in_paper/real_comparison.png)<br>
From top to bottom are images restored by Pan et al., Nah et al., Tao et al., Zhang et al. and ours. As space limits, the original blurry images are omitted here.
They can be viewed in Lai dataset with their names, from left to right: boy_statue, pietro, street4 and text1.
<br>
## Prerequisites
Please refer to "/code/requirements.txt".
<br>
## Installation
```
git clone https://github.com/minyuanye/SIUN.git
cd code
```
## Basic usage
You can always add '--gpu=<gpu_id>' to specify GPU ID, the default ID is 0.<br>
1. For deblurring an image:<br>
**python deblur.py --apply --file-path='</testpath/test.png>'**<br>
2. For deblurring all images in a folder:<br>
**python deblur.py --apply --dir-path='</testpath/testDir>'**<br>
Add '--result-dir=</output_path>' to specify output path. If it is not specified, the default path is './output'.<br>
3. For testing the model:<br>
**python deblur.py --test**<br>
Note that this command can only be used to test GOPRO dataset. And it will load all images into memory first. We recommand to use '--apply'
as an alternative (Item 2).<br>
Please set value of 'test_directory_path' to specify the GOPRO dataset path in file 'config.py'.<br>
4. For training a new model:<br>
**python deblur.py --train**<br>
Please remove the model file in 'model' first and set value of 'train_directory_path' to specify the GOPRO dataset path in file 'config.py'.<br>
When it finishes, run:<br>
**python deblur.py --verify**<br>
## Advanced usage
Please refer to the source code. Most configuration parameters are listed in '/code/src/config.py'.
## Citation
If you use any part of our code, or SIUN is useful for your research, please consider citing:
```bibtex
@ARTICLE{8963625,
author={M. {Ye} and D. {Lyu} and G. {Chen}},
journal={IEEE Access},
title={Scale-Iterative Upscaling Network for Image Deblurring},
year={2020},
volume={8},
number={},
pages={18316-18325},
keywords={Blind deblurring;curriculum learning;scale-iterative;upscaling network},
doi={10.1109/ACCESS.2020.2967823},
ISSN={2169-3536},
month={},}
```
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Scale-Iterative Upscaling Network for Image Deblurring 论文代码
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SIUN-master.zip (72个子文件)
SIUN-master
comparisons
real
text1_Tao.jpg 73KB
pietro_Zhang.jpg 33KB
street4_Nah.jpg 113KB
street4_Zhang.jpg 103KB
street4_Pan.jpg 172KB
pietro_Nah.jpg 34KB
boy_statue_Zhang.jpg 62KB
pietro_Tao.jpg 35KB
pietro_Ye.jpg 36KB
text1_Nah.jpg 79KB
street4_Tao.jpg 108KB
pietro_Pan.jpg 45KB
text1_Pan.jpg 150KB
street4_Ye.jpg 114KB
text1_Ye.jpg 76KB
boy_statue_Pan.jpg 88KB
boy_statue_Nah.jpg 64KB
text1_Zhang.jpg 72KB
boy_statue_Tao.jpg 63KB
boy_statue_Ye.jpg 66KB
benchmark
42_Nah.png 1.03MB
1030_Tao.png 1.05MB
Blurry2_1_Tao.png 730KB
629_Blur.png 757KB
Blurry2_1_Ye.png 712KB
Blurry2_1_Blur.png 738KB
629_Ye.png 687KB
629_Zhang.png 753KB
42_Blur.png 915KB
1030_Ye.png 1019KB
42_Zhang.png 900KB
629_Tao.png 776KB
629_Nah.png 934KB
42_Ye.png 904KB
Blurry2_1_Zhang.png 654KB
Blurry2_1_Nah.png 786KB
1030_Blur.png 970KB
1030_Nah.png 1.14MB
42_Tao.png 935KB
1030_Zhang.png 1023KB
building
building1_Zhang.jpg 97KB
building1_Tao.jpg 104KB
building1_Ye.jpg 111KB
building1_Nah.jpg 96KB
images_in_paper
real_comparison.png 2.7MB
Real_building1_comparison.png 475KB
benchmark_comparison.png 2.54MB
README.md 3KB
code
__init__.py 0B
src
__init__.py 0B
lib
__init__.py 0B
MLVSharpnessMeasure.py 2KB
tf_util.pyc 860B
data_helper.py 3KB
data_producer.py 2KB
__init__.pyc 136B
tf_util.py 631B
__pycache__
data_helper.cpython-36.pyc 3KB
__init__.cpython-36.pyc 132B
application.py 5KB
tester.py 5KB
trainer.py 7KB
model
__init__.py 0B
model.py 5KB
verification.py 9KB
__pycache__
__init__.cpython-36.pyc 128B
config.cpython-36.pyc 2KB
config.py 2KB
deblur.py 2KB
model
generator.json 90KB
generator.h5 24.46MB
requirements.txt 67B
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