Code of the paper "Morphological Networks for Image De-raining"

Input	De-Rained

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_morphonet.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