Decomposition Makes Better Rain Removal: An Improved Attention-guided Deraining Network (IADN)

This is an implementation of the IADN model proposed in the paper (Decomposition Makes Better Rain Removal: An Improved Attention-guided Deraining Network) with TensorFlow.

Requirements

• Python 3
• TensorFlow 1.12.0
• OpenCV
• tqdm
• glob
• sys

Usage

I. Train the IADN model

Dataset Organization Form

If you prepare your own dataset, please follow the following form:

|--train_data

|--rainysamples  
    |--file1
            ：  
    |--file2
        :
    |--filen
    
|--clean samples
    |--file1
            ：  
    |--file2
        :
    |--filen

Then you can produce the corresponding '.npy' in the '/dataset/train_data/npy' file.

$ python crop.py
$ python rescale.py
$ python preprocessing.py

Training

Download training dataset ((raw images)Baidu Cloud, (Password:4qnh) (.npy)Baidu Cloud, (Password:gd2s)), or prepare your own dataset like above form.

Run the following commands:

cd ./model
python train_IADN.py 

II. Test the IADN model

Test the Retraining Model With Your Own Dataset (TEST_IADN.PY)

Download the commonly used testing rain dataset (R100H, R100L, TEST100, TEST1200, TEST2800) (Google Drive), and the test results of other competing models can be downloaded from here (TEST1200, TEST100, R100H, R100L).

In addition, the test samples and the results of other low-level vision tasks can be downloaded form (dehazing, low-light).

Put your dataset in './test/test_data/* ' ( *denotes one of the tasks in [deraing, dehazing, and low-light enhancement]).

Select the special task, and then change the data path. Run the following commands:

cd ./model/test
python test_IADN.py

The deraining results will be in './test/test_data/*/IADN'.

Citation

@ARTICLE{9294056,
  author={K. {Jiang} and Z. {Wang} and P. {Yi} and C. {Chen} and Z. {Han} and T. {Lu} and B. {Huang} and J. {Jiang}},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Decomposition Makes Better Rain Removal: An Improved Attention-guided Deraining Network}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TCSVT.2020.3044887}}
@InProceedings{Kui_2020_CVPR,
	author = {Jiang, Kui and Wang, Zhongyuan and Yi, Peng and Chen, Chen and Huang, Baojin and Luo, Yimin and Ma, Jiayi and Jiang, Junjun},
	title = {Multi-Scale Progressive Fusion Network for Single Image Deraining},
	booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
	month = {June},
	year = {2020}
}