2d-morphological-network's Introduction

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_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

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