DGNL-Net and RainCityscapes

Single-Image Real-Time Rain Removal Based on Depth-Guided Non-Local Features

by Xiaowei Hu, Lei Zhu, Tianyu Wang, Chi-Wing Fu, and Pheng-Ann Heng

This implementation is written by Xiaowei Hu at the Chinese University of Hong Kong.

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Please find the code of the conference version at https://github.com/xw-hu/DAF-Net.

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RainCityscapes Dataset

Our RainCityscapes dataset is available for download at the Cityscapes website.

Citations

@article{hu2021single,                    
   title={Single-Image Real-Time Rain Removal Based on Depth-Guided Non-Local Features},                
   author={Hu, Xiaowei and Zhu, Lei and Wang, Tianyu and Fu, Chi-Wing and Heng, Pheng-Ann},               
   journal={IEEE Transactions on Image Processing},              
   volume={30},                
   pages={1759--1770},            
   year={2021}         
}

@InProceedings{Hu_2019_CVPR,      
  author = {Hu, Xiaowei and Fu, Chi-Wing and Zhu, Lei and Heng, Pheng-Ann},      
  title = {Depth-Attentional Features for Single-Image Rain Removal},      
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},      
  pages={8022--8031},      
  year = {2019}      
}

Prerequisites

• Python 3.5
• PyTorch 1.0

Installation

Clone this repository:

git clone https://github.com/xw-hu/DGNL-Net.git

Test

1. Please download our pretrained model at Google Drive.
   Put the model 40000.pth in ./ckpt/DGNLNet/.
   Put the model 60000.pth in ./ckpt/DGNLNet_fast/.

2. Test the DGNL-Net or DGNL-Net-fast:

   python3 infer.py    

   python3 infer_fast.py    

Train

1. Train the DGNL-Net model:

   python3 train.py    

2. Train the DGNL-Net-fast model:

   python3 train_fast.py

Evaluation

Please find the evaluation code at https://github.com/xw-hu/DAF-Net.
Enter the DAF-Net/examples/ and run evaluate_raincityscapes.m in Matlab.