Support for TensorFlow 2.x

README.md

@@ -1,24 +1,30 @@
 # Scale-recurrent Network for Deep Image Deblurring
+
 by [Xin Tao](http://www.xtao.website), Hongyun Gao, [Xiaoyong Shen](http://xiaoyongshen.me/), [Jue Wang](http://juew.org), [Jiaya Jia](http://www.cse.cuhk.edu.hk/leojia/). ([pdf](http://www.xtao.website/projects/srndeblur/srndeblur_cvpr18.pdf))
 
 ### Our results on real data
+
 <img src="./imgs/teaser.jpg" width="100%" alt="Real Photo">
 
 ### Results on the testing dataset
+
 <img src="./imgs/comp_sota.jpg" width="100%" alt="Testing Dataset">
 
 ### More cases on real photos from previous papers:
+
 <img src="./imgs/comp_real.jpg" width="100%" alt="More Cases">
 
 ## Prerequisites
-- Python2.7
-- Scipy
-- Scikit-image
-- numpy
-- Tensorflow 1.4 with NVIDIA GPU or CPU (cpu testing is very slow)
+
+-   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. 
+
+Clone this project to your machine.
 
 ```bash
 git clone https://github.com/jiangsutx/SRN-Deblur.git
@@ -27,50 +33,52 @@ cd SRN-Deblur
 
 ## Testing
 
-Download pretrained models through: `download_model.sh` inside `checkpoints/`.
+Download pretrained models through: `download_model.sh` inside `checkpoints/`.<br>
+In case it doesn't work, download the weights from <a href='http://www.xtao.website/shared_url/srndeblur_models.zip'>here</a>
 
-To test blur images in a folder, just use arguments 
+To test blur images in a folder, just use arguments
 `--input_path=<TEST_FOLDER>` and save the outputs to `--output_path=<OUTPUT_FOLDER>`.
 For example:
 
 ```bash
 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. 
+If you have a GPU, please include `--gpu` argument, and add your gpu id to your command.
+Otherwise, use `--gpu=-1` for CPU.
 
 ```bash
 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. 
+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 
+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, 
+According to our experience, `--height=720` and `--width=1280` work well
+on a Gefore GTX 1050 TI with 4GB memory. For example,
 
 ```bash
 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 
+
+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](https://github.com/SeungjunNah/DeepDeblur_release). 
-Please put the dataset into `training_set/`. And the provided `datalist_gopro.txt` 
-can be used to train the model. 
+We trained our model using the dataset from
+[DeepDeblur_release](https://github.com/SeungjunNah/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:
 
@@ -79,23 +87,28 @@ 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.
+   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. 
+   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.
+   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.  
+
+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: 
+
+If you use any part of our code, or SRN-Deblur is useful for your research, please consider citing:
 
 ```bibtex
 @inproceedings{tao2018srndeblur,
@@ -107,16 +120,18 @@ If you use any part of our code, or SRN-Deblur is useful for your research, plea
 ```
 
 ## Contact
+
 We are glad to hear if you have any suggestions and questions.
 
 Please send email to xtao@cse.cuhk.edu.hk
 
 ## 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.
+[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.
+[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.