AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring (CVPR2024)

Xintian Mao, Qingli Li and Yan Wang

Paper: https://github.com/INVOKERer/AdaRevD/blob/master/AdaRevD.pdf

Quick Run

Training

1. Download GoPro training and testing data
2. To train the main body of AdaRevD, download the pretrained model from NAFNet or UFPNet, modify state_dict_pth_encoder in GoPro-AdaRevIDB-pretrain-4gpu.yml and run

cd AdaRevD
./train_4gpu.sh Motion_Deblurring/Options/GoPro-AdaRevIDB-pretrain-4gpu.yml

3. To train the classifier of AdaRevD, modify the pretrain_network_g in GoPro-AdaRevIDB-classify-4gpu.yml and run

./train_4gpu.sh Motion_Deblurring/Options/GoPro-AdaRevIDB-classify-4gpu.yml

Evaluation

To test the pre-trained models 百度网盘(提取码:dfce) on your own images, run

python Motion_Deblurring/val.py 

Results

Results on GoPro, HIDE, Realblur test sets: 百度网盘(提取码:27ex)

Citation

If you use , please consider citing:

@inproceedings{xintm2024AdaRevD, 
    title = {AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring},
    author = {Xintian Mao, Qingli Li and Yan Wang}, 
    booktitle = {Proc. CVPR}, 
    year = {2024}
    }

Contact

If you have any question, please contact mxt_invoker1997@163.com

Our Related Works

• Deep Residual Fourier Transformation for Single Image Deblurring, arXiv 2021. Paper | Code
• Intriguing Findings of Frequency Selection for Image Deblurring, AAAI 2023. Paper | Code

Reference Code:

• https://github.com/Fangzhenxuan/UFPDeblur
• https://github.com/megvii-research/NAFNet
• https://github.com/megvii-research/RevCol
• https://github.com/littlepure2333/APE
• https://github.com/INVOKERer/DeepRFT/tree/AAAI2023

Acknowledgment: This code is based on the BasicSR toolbox.