This repository contains the official Tensorflow implementation of the Opening-Closing network as described in the paper Image Restoration by Learning Morphological Opening-Closing Network (Medium Blog)
In this work, we revisit the concept of structuring elements (SE) for morphological operations and attempt to incorporate it with learning-based methods. We propose the Opening-Closing network, consisting solely of basic morphological operations such as Opening and Closing, for de-raining and de-hazing real-life images. Unlike convolutions, morphological operations have inherent non-linearity that helps extract interpretable features and produce complex decision boundaries. These properties allow our network to get results comparable to other state of the art methods, despite having a fraction of parameters.
├── src_haze # directory contains the code for color image dehazing
├── src_rain # directory contains the code for color/grayscale image de-raining
├── src_shape # directory contains the code for simulation
Real rainy images |
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Real hazy images |
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Please cite our paper if you find the code useful.
@inproceedings{mondal2020image,
title={Image Restoration by Learning Morphological Opening-Closing Network },
author={Mondal, Ranjan and Shankar Dey, Moni and Chanda, Bhabatosh},
journal={Mathematical Morphology-Theory and Applications},
number={1},
pages={87--107},
year={2020},
publisher={De Gruyter}
}
@inproceedings{mondal2019morphological,
title={Morphological networks for image de-raining},
author={Mondal, Ranjan and Purkait, Pulak and Santra, Sanchayan and Chanda, Bhabatosh},
booktitle={International Conference on Discrete Geometry for Computer Imagery},
pages={262--275},
year={2019},
organization={Springer}
}