Code of the paper "Morphological Networks for Image De-raining"
| Input | De-Rained |
|---|---|
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Dependency
- For Running
- Python2
- keras(tensorflow-backend with channel last)
- scipy
- numpy
- scikit-image
- matplotlib
- imageio
Running
$ cd src/
$ python run.py <rainy_image_dir> <output_dir>
This runs the code in the supplied images.
$python run.py ../data/input_images/ ../data/output/
Files
├── data
│ ├── input_images
│ │ └── 52_in.png
│ └── output
│ ├── 52_4small.png
│ └── GT_p2small_morpho_net_.jpg
├── models #all the Trained model weights saved here
│ ├── model_cnn.h5
│ ├── MorphoN.h5
│ ├── MorphoN_small.h5
│ ├── path1.h5
│ ├── path2.h5
│ ├── weights_cnn.h5
│ ├── weights_morphoN.h5
│ ├── weights_morphoN_small.h5
│ ├── weights_path1.h5
│ └── weights_path2.h5
├── Readme.md
└── src
├── generator.py #generates Data for training
├── init.py #place Rainy dataset here For training
├── models.py #All the Defination of model
├── morph_layers2D.py #2D morphological Network
├── run.py #main run file
└── utils.py #other files
Publication
Ranjan Mondal,Pulak Purkiat, Sanchayan Santra and Bhabatosh Chanda. "Morphological Networks for Image De-raining" Discrete Geometry for Computer Imagery, 2019
#If you are using this code please cite the paper