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Official Pytorch Code for "Exploring Overcomplete Representations for Single Image Deraining using CNNs" - IEEE Journal of STSP

License: MIT License

Python 99.02% MATLAB 0.98%

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Derain_OUCD_Net

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

Paper

Prerequisites

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

Pytorch Installation

Datasets-Link:

Using the Code

Clone the repository

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

Dataset structure

Download the rain datasets and arrange the rainy images and clean images in the following order
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

Choosing the dataset

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'

Training Command

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

Testing Command

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

Citation

@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}
}

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