Awesome
UDGNet
Unsupervised Image Deraining: Optimization Model Driven Deep CNN Paper
By Changfeng Yu*, Yi Chang* (https://scholar.google.com/citations?user=I1nZ67YAAAAJ&hl=en)(* indicates equal contribution)
Demo
Dependencies and Installation
- Python 3 (Recommend to use Anaconda)
- PyTorch >= 1.2
- NVIDIA GPU + CUDA
- Python packages:
pip install -r enviroment.txt
Dataset
- Our dataset RealRain(including the angle) and Rain_cityscape can be downloaded here.
- Make your own data:
- Simulating by yourself, the angle information can be easily obtained during the simulation.
- Other sythetic datasets or real image dataset, you need to label the angle informations by ./lib/angle_label.py
- the dataset should have the following structure:
-train -rain/data -angle/data -clean/data
-test -rain/data -angle/data
- the training data based cityscape can be downlowed form Baidu Netdisk the extraction code is hust
How to Train
- UDGNet
- Run command:
python train_Decomposition_angle.py --rain_path ./dataset/test/rain --angle_path ./data/test/angle --clean_path ./data/test/rain --reset 1
How to Test
- UDGNet
- Run command:
python Test_Decomposition_angle.py --rain_path ./dataset/test/rain --angle_path ./data/test/angle --clean_path ./data/test/rain --weight_path ./output/real_model/generator_backup.pth