[Paper Link] (CVPR'18)
We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with dif- ferent scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network.
@inproceedings{derain_zhang_2018,
title={Density-aware Single Image De-raining using a Multi-stream Dense Network},
author={Zhang, He and Patel, Vishal M},
booktitle={CVPR},
year={2018}
}
- Linux
- Python 2 or 3
- CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
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Install PyTorch and dependencies from http://pytorch.org (Ubuntu+Python2.7) (conda install pytorch torchvision -c pytorch)
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Install Torch vision from the source. (git clone https://github.com/pytorch/vision cd vision python setup.py install)
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Install python package: numpy, scipy, PIL, pdb
python test.py --dataroot ./facades/github --valDataroot ./facades/github --netG ./pre_trained/netG_epoch_9.pth
Pre-trained model can be downloaded at (put it in the folder 'pre_trained'): https://drive.google.com/drive/folders/1VRUkemynOwWH70bX9FXL4KMWa4s_PSg2?usp=sharing
Pre-trained density-aware model can be downloaded at (Put it in the folder 'classification'): https://drive.google.com/drive/folders/1-G86JTvv7o1iTyfB2YZAQTEHDtSlEUKk?usp=sharing
Pre-trained residule-aware model can be downloaded at (Put it in the folder 'residual_heavy'): https://drive.google.com/drive/folders/1bomrCJ66QVnh-WduLuGQhBC-aSWJxPmI?usp=sharing
Training will be released soon.
python demo.py --dataroot ./your_dataroot --valDataroot ./your_dataroot --netG ./pre_trained/netG_epoch_9.pth
Training and testing data will be released soon.
Great thanks for the insight discussion with Vishwanath Sindagi and help from Hang Zhang