SRN-Deblur

by jiangsutx

jiangsutx / SRN-Deblur

Repository for Scale-recurrent Network for Deep Image Deblurring

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Scale-recurrent Network for Deep Image Deblurring

by Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, Jiaya Jia. (pdf)

Our results on real data

Real Photo

Results on the testing dataset

Testing Dataset

More cases on real photos from previous papers:

More Cases

Prerequisites

  • Python2.7
  • Scipy
  • Scikit-image
  • numpy
  • Tensorflow 1.4 with NVIDIA GPU or CPU (cpu testing is very slow)

Installation

Clone this project to your machine.

git clone https://github.com/jiangsutx/SRN-Deblur.git
cd SRN-Deblur

Testing

Download pretrained models through:

download_model.sh
inside
checkpoints/
.

To test blur images in a folder, just use arguments

--input_path=
and save the outputs to
--output_path=
. For example:
python run_model.py --input_path=./testing_set --output_path=./testing_res

If you have a GPU, please include

--gpu
argument, and add your gpu id to your command. Otherwise, use
--gpu=-1
for CPU.
python run_model.py --gpu=0

To test the model, pre-defined height and width of tensorflow placeholder should be assigned. Our network requires the height and width be multiples of

16
. When the gpu memory is enough, the height and width could be assigned to the maximum to accommodate all the images.

Otherwise, the images will be downsampled by the largest scale factor to be fed into the placeholder. And results will be upsampled to the original size.

According to our experience,

--height=720
and
--width=1280
work well on a Gefore GTX 1050 TI with 4GB memory. For example,
python run_model.py --height=720 --width=1280

Evaluation

The quantitative results of PSNR and SSIM in the paper is calculated using MATLAB built-in function

psnr()
and
ssim()
based on the generated color results.

Training

We trained our model using the dataset from DeepDeblur_release. Please put the dataset into

training_set/
. And the provided
datalist_gopro.txt
can be used to train the model.

Hyper parameters such as batch size, learning rate, epoch number can be tuned through command line:

python run_model.py --phase=train --batch=16 --lr=1e-4 --epoch=4000

Models

We provided 3 models (training settings) for testing: 1.

--model=lstm
: This model implements exactly the same structure in our paper. Current released model weights should produce
PSNR=30.19, SSIM=0.9334
on GOPRO testing dataset. 2.
--model=gray
: According to our further experiments after paper acceptance, we are able to get a slightly better model by tuning parameters, even without LSTM. This model should produce visually sharper and quantitatively better results. 3.
--model=color
: Previous models are trained on gray images, and may produce color ringing artifacts. So we train a model directly based on RGB images. This model keeps better color consistency, but the results are less sharp.

How to choose

If you would like to compare performance against our method, you can use model

gray
and
lstm
. If you want to restore blurry images you can try
gray
and
color
. And
color
is very useful in low-light noisy images.

Reference

If you use any part of our code, or SRN-Deblur is useful for your research, please consider citing:

@inproceedings{tao2018srndeblur,
  title={Scale-recurrent Network for Deep Image Deblurring},
  author={Tao, Xin and Gao, Hongyun and Shen, Xiaoyong and Wang, Jue and Jia, Jiaya},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}

Contact

We are glad to hear if you have any suggestions and questions.

Please send email to [email protected]

Reference

[1]

Sun et al.
J. Sun, W. Cao, Z. Xu, and J. Ponce. Learning a convolutional neural network for non-uniform motion blur removal. In CVPR, pages 769–777. IEEE, 2015.

[2]

Nah et al.
S. Nah, T. H. Kim, and K. M. Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. pages 3883–3891, 2017.

[3]

Whyte et al.
O. Whyte, J. Sivic, A. Zisserman, and J. Ponce. Nonuniform deblurring for shaken images. International Journal on Computer Vision, 98(2):168–186, 2012.

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