Stripformer: Strip Transformer for Fast Image Deblurring (ECCV 2022 Oral)

Pytorch Implementation of "Stripformer: Strip Transformer for Fast Image Deblurring"

Installation

The implementation of our BANet is modified from "DeblurGANv2"

git clone https://github.com/pp00704831/Stripformer.git
cd Stripformer
conda create -n Stripformer python=3.6
source activate Stripformer
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install opencv-python tqdm pyyaml joblib glog scikit-image tensorboardX albumentations
pip install -U albumentations[imgaug]
pip install albumentations==1.1.0

Training

Download "GoPro" dataset into './datasets'
For example: './datasets/GoPro/train/blur/**/*.png'

We train our Stripformer in two stages:
1) We pre-train Stripformer for 3000 epochs on patch size 256x256. Please run the following commands.

python train_Stripformer_pretrained.py

2) After stage 1, we keep training Stripformer for 1000 epochs on patch size 512x512. Please run the following commands.

python train_Stripformer_gopro.py

Testing

For reproducing our results on GoPro and HIDE dataset, download the "Stripformer_gopro.pth"

For reproducing our results on RealBlur dataset, download "Stripformer_realblur_J.pth" and "Stripformer_realblur_R.pth"

• For testing on GoPro test set
  Download "GoPro" full dataset or test set into './datasets'
  For example: './datasets/GoPro/test/blur/**/*.png'

python predict_GoPro_test_results.py --weights_path ./Stripformer_gopro.pth 

• For testing on HIDE dataset
  Download "HIDE" into './datasets'
  

python predict_HIDE_results.py --weights_path ./Stripformer_gopro.pth 

• For testing on RealBlur test sets Download "RealBlur_J" and "RealBlur_R" into './datasets'
  

python predict_RealBlur_J_test_results.py --weights_path ./Stripformer_realblur_J.pth 

python predict_RealBlur_R_test_results.py --weights_path ./Stripformer_realblur_R.pth 

• For testing your own training weight (take GoPro for a example)

1. Rename the path in line 23 in the predict_GoPro_test_results.py
   
2. Chage command to --weights_path ./final_Stripformer_gopro.pth

Evaluation

• For evaluation on GoPro results in MATLAB, download "Stripformer_GoPro_results" into './out'

evaluation_GoPro.m

• For evaluation on HIDE results in MATLAB, download "Stripformer_HIDE_results" into './out'

evaluation_HIDE.m

• For evaluation on RealBlur_J results, download "Stripformer_realblur_J_results" into './out'

python evaluate_RealBlur_J.py

• For evaluation on RealBlur_R results, download "Stripformer_realblur_R_results" into './out'

python evaluate_RealBlur_R.py