UMRL--using-Cycle-Spinning

Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining

Rajeev Yasarla, Vishal M. Patel

Paper Link (CVPR'19)

@InProceedings{Yasarla_2019_CVPR,
    author = {Yasarla, Rajeev and Patel, Vishal M.},
    title = {Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2019}
}

We present a novel Uncertainty guided Multi-scale Residual Learning (UMRL) network to address the single image de-raining. The proposed network attempts to address this issue by learning the rain content at different scales and using them to estimate the final de-rained output. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate. Furthermore, we introduce a new training and testing procedure based on the notion of cycle spinning to improve the final de-raining performance.

Prerequisites:

1. Linux
2. Python 2 or 3
3. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)

To test UMRL:

python umrl_test.py --dataroot ./facades/validation --valDataroot ./facades/validation --netG ./pre_trained/Net_DIDMDN.pth

To train UMRL:

python umrl_train.py --dataroot <dataset_path> --valDataroot ./facades/validation --exp ./check --netG ./pre_trained/Net_DIDMDN.pth

To test UMRL using Cycle Spining:

python umrl_cycspn_test.py --dataroot ./facades/validation --valDataroot ./facades/validation --netG ./pre_trained/Net_DIDMDN.pth

To train UMRL using Cycle Spining:

python umrl_cycspn_train.py --dataroot <dataset_path> --valDataroot ./facades/validation --exp ./check --netG ./pre_trained/Net_DIDMDN.pth

Acknowledgments

Thanks for the discussions with, and help from He Zhang