WRCAN-PyTorch

The official implementation of WRCAN (Wide Receptive Field and Channel Attention Network for Deblurring of JPEG compressed Image)[pdf], which is ranked in the 3rd, in the NTIRE 2021 challenge on Image Deblurring Track 2 JPEG artifacts.

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

WRCAN
@InProceedings{Lee_2021_CVPR,
    author    = {Lee, Donghyeon and Lee, Chulhee and Kim, Taesung},
    title     = {Wide Receptive Field and Channel Attention Network for JPEG Compressed Image Deblurring},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {304-313}
}

EDSR
@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

Dependencies

This source code is base on the PyTorch implementation of EDSR. Therefore, WRCAN-PyTorch follows the dependencies of the implementation of EDSR. For more information, please refer to EDSR-PyTorch.

• Python 3.8
• PyTorch >= 1.7
• numpy
• skimage
• imageio
• matplotlib
• tqdm
• cv2 >= 3.xx (Only if you want to use video input/output)

Testing REDS dataset for deblurring JPEG

Place unzipped REDS dataset test images in --dir_data folder (like <dir_data>/REDS/test/test_blur_jpeg/<sequence_name>/<image_name>).

For Deblur Track2 in NTIRE2021, you can download REDS dataset using this script here. Pre-trained model, which produces the results submitted to NTIRE challenge, can be downloaded from model_cvprw21 The inference result images on the validation set and the test set can be downloaded from val_results and test_results, respectively.

Using Jupyter notebook

Run the main.ipynb in the src folder. Before running the notebook file, please uncomment the options below "Test options" in the second code block and set the args.data_test appropriately.

args.n_GPUs = 4
args.model = 'DeblurUNet'
args.act = 'relu'
args.scale = '1'
args.rgb_range = 1
args.n_depth = 2
args.n_resgroups = 4
args.n_resblocks = 16
args.data_test = 'REDS_TEST' # 'REDS_TEST' is for the testset, 'REDS_VAL' is for the validation set.
args.save = 'deblur_jpeg/wrcan'
args.precision = 'amp'
args.chop = True

# Test options
args.reset = False
args.data_range = '1-3000'
args.test_only = True
args.pre_train = os.path.join('../experiment', args.save, 'model/model_best_patch320_epoch374.pt')
args.save_results = True
args.self_ensemble = True

Using command line

Run the script in src folder. Before you run the deblur, please uncomment the appropriate line in deblur.sh that you want to execute.

cd src       # You are now in */WRCAN-PyTorch/src
sh deblur.sh

Training REDS dataset for deblurring JPEG

We trained WRCAN using jupyter notebook. But, training the model using command line is also possible by setting appropriate arguments.

Generate JPEG-compressed HR images.

With src/data/reds_gen_jpeg.py, JPEG-compressed HR images are generated.

Using Jupter notebook

Run the main.ipynb in the src folder. Please comment test options in the second block and set appropriate options for training. An example is described below.

args.n_GPUs = 4
args.model = 'DeblurUNet'
args.act = 'relu'
args.scale = '1'
args.rgb_range = 1
args.n_depth = 2
args.n_resgroups = 4
args.n_resblocks = 16
args.data_train = 'REDS'
args.data_test = 'REDS_VAL'
args.data_range = '1-24000/1-100'
args.epochs = 400
args.loss = '1*L1+0.1*AE'
args.patch_size = 320
args.save = 'deblur_jpeg/wrcan'
args.save_results = False
args.reset = False
args.ch_shuffle = True
args.lr_noise_sigma = 2
args.decay = '100-200-250-300-350'
args.precision = 'amp'
args.chop = True
args.weight_decay = 1e-8