Official Pytorch Code for "Exploring Overcomplete Representations for Single Image Deraining using CNNs" - IEEE Journal of Selected Topics in Signal Processing

Paper

• Python >= 3.6
• Pytorch >= 1.0
• Torchvision >= 0.2.2
• Numpy >= 1.14.3

Pytorch Installation

1. Rain800
2. JORDER
3. SPANet

git clone https://github.com/jeya-maria-jose/Derain_OUCD_Net
cd Derain_OUCD_Net

1. Download the rain datasets and arrange the rainy images and clean images in the following order
2. Save the image names into text file (dataset_filename.txt)

   .
    ├── data 
    |   ├── train # Training  
    |   |   ├── derain        
    |   |   |   ├── <dataset_name>   
    |   |   |   |   ├── rain              # rain images 
    |   |   |   |   └── norain            # clean images
    |   |   |   └── dataset_filename.txt
    |   └── test  # Testing
    |   |   ├── derain         
    |   |   |   ├── <dataset_name>          
    |   |   |   |   ├── rain              # rain images 
    |   |   |   |   └── norain            # clean images
    |   |   |   └── dataset_filename.txt

Mention the txt file of the dataset in line 13 of train_data.py and val_data.py, for example

   train_list = train_data_dir + train_filename + "rain800.txt"
   val_list = val_data_dir + 'val_list_rain800.txt'

python train.py -net OUCD -category derain -train_batch_size 2 -save_dir rain800_OUCD -num_epochs 200

Choose the model you want to load from the checkpoint. Change the epoch and bestp variables with the model you need to test. Then, run

python test.py -category derain -exp_name OUCD

@misc{yasarla2020exploring,
      title={Exploring Overcomplete Representations for Single Image Deraining using CNNs}, 
      author={Rajeev Yasarla and Jeya Maria Jose Valanarasu and Vishal M. Patel},
      year={2020},
      eprint={2010.10661},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}