Pytorch Implementation of "BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring"
The implementation of our BANet is modified from "DeblurGANv2"
git clone https://github.com/pp00704831/BANet.git
cd BANet
conda create -n banet python=3.6
source activate banet
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
pip install opencv-python tqdm pyyaml joblib glog scikit-image tensorboardX albumentations
pip install -U albumentations[imgaug]
pip install albumentations==1.1.0
Download "GoPro" dataset into './datasets'
For example:
'./datasets/GoPro/train/blur/**/*.png'
We train our BANner in two stages:
1) We pre-train for 3000 epochs on patch size 256x256. Please run the following commands.
python pretrained.py
2) We fine-tune for 1000 epochs on patch size 512x512. Please run the following commands.
python train.py
Download "GoPro" dataset into './datasets'
For example:
'./datasets/GoPro/test/blur/**/*.png'
For reproducing our results, download the GoPro trained model "BANet_GoPro.pth
For testing on GoPro dataset
python predict_BANet_GoPro_test_results.py --weights_path ./BANet_GoPro.pth
For testing on HIDE dataset
Download "HIDE" dataset into './datasets'
python predict_BANet_HIDE_test_results.py --weights_path ./BANet_GoPro.pth
For testing your own training weight on GoPro or HIDE
Take GoPro for example
- Rename the 'output_path' in line 23 in the predict_BANet_GoPro_test_results.py
- Chage weight path command to --weights_path ./final_BANet_GoPro.pth
For evaluation on GoPro results in MATLAB Download "BANet_GoPro_result" into './out'
evaluation_GoPro.m
For evaluation on HIDE results in MATLAB Download "BANet_HIDE_result" into './out'
evaluation_HIDE.m
The training strategy is the same as BANet above.
Download the GoPro trained model "BANet_Plus_GoPro.pth
For testing on GoPro dataset
python predict_BANet_Plus_GoPro_test_results.py --weights_path ./BANet_Plus_GoPro.pth
For testing on HIDE dataset
python predict_BANet_Plus_HIDE_test_results.py --weights_path ./BANet_Plus_GoPro.pth
@ARTICLE{Tsai2022BANet,
author={Tsai, Fu-Jen and Peng, Yan-Tsung and Tsai, Chung-Chi and Lin, Yen-Yu and Lin, Chia-Wen},
journal={IEEE Transactions on Image Processing},
title={BANet: A Blur-Aware Attention Network for Dynamic Scene Deblurring},
year={2022},
volume={31},
pages={6789-6799},
}