We further extend the efficientderain in https://github.com/tsingqguo/efficientderain via a novel predictive filtering framework. This work has been accepted by IJCV. More details could be found in our pre-print version: https://arxiv.org/abs/2201.02366.

• python 3.6
• pytorch 1.6.0
• opencv-python 4.4.0.44
• scikit-image 0.17.2
• torchvision 0.9.1
• pytorch-msssim 0.2.1

• Rain100L-old_version: https://github.com/nnUyi/DerainZoo/blob/master/DerainDatasets.md
• Rain100H-old_version: https://github.com/nnUyi/DerainZoo/blob/master/DerainDatasets.md
• Rain1400: https://xueyangfu.github.io/projects/cvpr2017.html
• SPA: https://stevewongv.github.io/derain-project.html
• Raindrop: https://paperswithcode.com/dataset/raindrop
• NTURain dataset: https://github.com/hotndy/SPAC-SupplementaryMaterials

Here is the urls of pretrained models included models :

direct download: Coming soon.

google drive: Coming soon.

baiduyun: Coming soon.

• The code shown corresponds to version v3, for v4 change the value of argument "rainaug" in file "./train.sh" to the "true"
• Change the value of argument "baseroot" in file "./train.sh" to the path of training data
• Edit the function "get_files" in file "./utils" according to the format of the training data
• Execute

sh train.sh

• The code shown corresponds to version v3
• Change the value of argument "load_name" in file "./test.sh" to the path of pretained model
• Change the value of argument "baseroot" in file "./test.sh" to the path of testing data
• Edit the function "get_files" in file "./utils" according to the format of the testing data
• Execute

sh test.sh

@article{guo2024learning,
  title={Learning Uncertainty-Aware Filtering via RainMix Augmentation for High-Efficiency Deraining},
  author={Guo, Qing and Qi, Hua and Sun, Jingyang and Juefei-Xu, Felix and Ma, Lei and Lin, Di and Feng, Wei and Wang, Song
},
  journal={International journal of computer vision},
  year={2024}
}