[paper:https://ieeexplore.ieee.org/document/9388918]
Methods of rain removal based on deep learning have rapidly developed, and the image quality after rain removal is continuously improving. However, the results of most methods have some common problems, including a loss of details, a blurring of edges, and the existence of artifacts. To remove rain-related information more thoroughly and retain more edge details, this paper proposes an end-to-end rain removal network based on the progressive residual detail supplement (ERRN-PRDS) approach. The entire network structure is designed in an iterative manner to obtain higher-quality rain removal images from coarse to fine. In the network, a diamond residual block is constructed as the main module of iteration to learn the feature information of the background layer. Meanwhile, to keep more texture details in the background layer, a detail supplement mechanism is designed between the iterative layers to transfer more information to the next iterative operation. Experimental results show that this method can remove the rain information more completely and better retain the image edges compared with previous state-of-the-art methods. In addition, because of the sparsity of the detail injection, our network also achieves high-quality results for image denoising tasks.
- Python 3.6, PyTorch >= 0.4.0
- Requirements: opencv-python, tensorboardX
- MATLAB for computing evaluation metrics
To train the models, please download training datasets: https://github.com/nnUyi/DerainZoo
We have provided our pre-trained models in ./logs/.
diedai10 is provided for heavy rain, diedai10_L is provided for light rain.
Please change your own parameters in code, like --logdir --data_path --save_path ......
Run python scripts to test the models:
python test.py # test models on synthetic datasetyou can directly compute all the evaluation metrics in this paper.
We also provide the MATLAB scripts to compute the average PSNR and SSIM values reported in the paper.
cd ./statistic
run statistic_Rain100H.m
run statistic_Rain100L.m
run statistic_Rain12.mAverage PSNR/SSIM values on three datasets:
| Dataset | RESCAN | PReNet | Ours |
|---|---|---|---|
| Rain100H | 28.82/0.863 | 29.46/0.899 | 31.67/0.927 |
| Rain100L | 39.22/0.983 | 37.48/0.979 | 41.02/0.989 |
| Rain12 | 34.79/0.958 | 36.15/0.969 | 36.99/0.971 |
Run python scripts to train the models:
python train.py Please change your own parameters in code.
You can use tensorboard --logdir ./logs/your_model_path to check the training procedures.
The following tables provide the configurations of options.
| Option | Default | Description |
|---|---|---|
| batchSize | 12 | Training batch size |
| recurrent_iter | 10 | Number of recursive stages |
| epochs | 100 | Number of training epochs |
| milestone | [30,50,80] | When to decay learning rate |
| lr | 1e-3 | Initial learning rate |
| save_freq | 5 | save intermediate model |
| use_GPU | True | use GPU or not |
| gpu_id | 0 | GPU id |
| data_path | N/A | path to training images |
| save_path | N/A | path to save models and status |
| Option | Default | Description |
|---|---|---|
| use_GPU | True | use GPU or not |
| gpu_id | 0 | GPU id |
| recurrent_iter | 10 | Number of recursive stages |
| logdir | N/A | path to trained model |
| data_path | N/A | path to testing images |
| save_path | N/A | path to save results |
[1] Ren, Dongwei and Zuo, Wangmeng and Hu, Qinghua and Zhu, Pengfei and Meng, Deyu. Progressive Image Deraining Networks: A Better and Simpler Baseline. In IEEE CVPR 2019.
Part of our code is borrowed from PReNet(https://github.com/csdwren/PReNet), Thanks for the sharing of codes by Dongwei Ren.
@ARTICLE{9388918,
author={Yang, Yong and Guan, Juwei and Huang, Shuying and Wan, Weiguo and Xu, Yating and Liu, Jiaxiang},
journal={IEEE Transactions on Multimedia},
title={End-to-End Rain Removal Network Based on Progressive Residual Detail Supplement},
year={2022},
volume={24},
number={},
pages={1622-1636},
doi={10.1109/TMM.2021.3068833}}