README.md

Realistic Blur Synthesis for Learning Image Deblurring

Project | Paper | Supple

Official Implementation of ECCV Paper

Realistic Blur Synthesis for Learning Image Deblurring
Jaesung Rim, Geonung Kim, Jungeon Kim, Junyong Lee, Seungyong Lee, Sunghyun Cho.
POSTECH
IEEE European Conference on Computer Vision (ECCV) 2022

Pytorch implementation

We provide simple Dataset modules for adopting our pipeline. This is slightly different from the tensorflow implementation.

Please refer to Uformer-RSBlur.

# ./Uformer-RSBlur/dataset/dataset_RealisticDeblur.py

class RealisticGoProABMEDataset(Dataset):
    def __init__(self, image_dir, patch_size=256, image_aug=True, realistic_pipeline=True):
        ...

    def __len__(self):
        return len(self.image_list)

    def __getitem__(self, idx):
        ...

class RealisticGoProUDataset(Dataset):
    def __init__(self, image_dir, patch_size=256, image_aug=True, realistic_pipeline=True):
        ...

    def __len__(self):
        return len(self.image_list)

    def __getitem__(self, idx):
        ...

Results with the proposed method.

Installation

git clone https://github.com/rimchang/RSBlur.git

Tested environment

We recommend a virtual environment using conda or docker.

SRN-Deblur

• Tensorflow 1.15
• cuda11.1

Download

Dataset [Google Drive] [Postech]

# RSBlur.zip

RSBlur
├── 0001
│   ├── 000001
│   │   ├── real_blur/real_blur.png # real blurred image
│   │   ├── avg65_img/avg_blur.png # synthetic blurred image using frame interpolation
│   │   ├── avg65_mask_100/avg_blur.png # saturation mask
│   │   ├── gt/gt_sharp.png # ground truth sharp image
...

# GoPro_INTER_ABME.zip

GoPro_INTER_ABME
├── GOPR0372_07_00 
│   ├── 000001
│   │   ├── avg_inter_img/avg_blur.png # synthetic blurred image using frame interpolation
│   │   ├── avg_inter_mask_100/avg_blur.png # saturation mask
│   │   ├── gt/gt_sharp.png # ground truth sharp image
...

# GoPro_U.zip

GoPro_U
├── centroid_blurred_img
│   ├── 0_GOPR0372_07_00_000047_003754_kernel_25_blurred.png # synthetic blurred image using a synthetic blur kernel
│   ...
├── centroid_blurred_mask_100
│   ├── 0_GOPR0372_07_00_000047_003754_kernel_25_blurred.png # saturation mask
│   ...
├── target_img
│   ├── 0_GOPR0372_07_00_000047_003754_kernel_25_gt.png # ground truth sharp image
│   ...

Dataset splits [link]

Pre-trained models [Google Drive] [Postech]

Training

# ./SRN-Deblur-RSBlur
# All datasets should be located in SRN-Deblur-RSBlur/dataset

# RSBlur
python run_model.py --phase=train --checkpoint_path=0719_SRN-Deblur_RSBlur_real --sat_synthesis=None --noise_synthesis=None --datalist=../datalist/RSBlur/RSBlur_real_train.txt --gpu=0
python run_model.py --phase=train --checkpoint_path=0719_SRN-Deblur_RSBlur_syn_with_ours --sat_synthesis=sat_synthesis --noise_synthesis=poisson_RSBlur --cam_params_RSBlur=1 --datalist=../datalist/RSBlur/RSBlur_syn_train.txt --gpu=0

# GoPro_INTER_ABME
python run_model.py --phase=train --checkpoint_path=0719_SRN-Deblur_GoPro_ABME_with_ours --target_dataset=RealBlur --sat_synthesis=sat_synthesis --noise_synthesis=poisson_gamma --cam_params_RealBlur=1 --adopt_crf_realblur=1 --datalist=../datalist/GoPro/GoPro_INTER_ABME_train.txt --gpu=0

# GoPro_U
python run_model.py --phase=train --checkpoint_path=0719_SRN-Deblur_U_with_ours --target_dataset=RealBlur --sat_synthesis=sat_synthesis --noise_synthesis=poisson_gamma --cam_params_RealBlur=1 --adopt_crf_realblur=1 --datalist=../datalist/GoPro/GoPro_U_train.txt --gpu=0

Testing

# ./SRN-Deblur-RSBlur
# All datasets should be located in SRN-Deblur-RSBlur/dataset

# RSBlur
python run_model.py --phase=test --checkpoint_path=SRN-Deblur_RSBlur_real --datalist=../datalist/RSBlur/RSBlur_real_test.txt --gpu=0
python run_model.py --phase=test --checkpoint_path=SRN-Deblur_RSBlur_syn_with_ours --datalist=../datalist/RSBlur/RSBlur_real_test.txt --gpu=0

# RealBlur
python run_model.py --phase=test --checkpoint_path=SRN-Deblur_GoPro_ABME_with_ours --datalist=../datalist/RealBlur_J_test_list.txt --gpu=0
python run_model.py --phase=test --checkpoint_path=SRN-Deblur_GoPro_U_with_ours --datalist=../datalist/RealBlur_J_test_list.txt --gpu=0

Evaluation

# ./evaluation

python evaluate_RSBlur.py --input_dir=../SRN-Deblur-RSBlur/testing_res/SRN-Deblur_RSBlur_real --gt_root=../SRN-Deblur-RSBlur/dataset/RSBlur;
python evaluate_RealBlur.py --input_dir=../SRN-Deblur-RSBlur/testing_res/SRN-Deblur_U_with_ours --gt_root=../SRN-Deblur-RSBlur/dataset/RealBlur-J_ECC_IMCORR_centroid_itensity_ref;

Real-world Deblurring Benchmark

We provide an additional deblurring benchmark to provide the basis for future deblurring research. All below models are trained on real blurred images of the RSBlur training set.

License

The RSBlur dataset is released under CC BY 4.0 license.

Acknowledment

The code is based on SRN-Deblur, CBDNet and UID.

Citation

If you use our dataset for your research, please cite our paper.

@inproceedings{rim_2022_ECCV,
 title={Realistic Blur Synthesis for Learning Image Deblurring},
 author={Rim, Jaesung and Kim, Geonung and Kim, Jungeon and Lee, Junyong and Lee, Seungyong and Cho, Sunghyun},
 booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
 year={2022}
}