This is the implementation of our WACV 2019 paper "Gated Context Aggregation Network for Image Dehazing and Deraining" by Dongdong Chen, Mingming He, Qingnan Fan, et al.
In this paper, we propose a new end-to-end gated context aggregation network GCANet for image dehazing, in which the smoothed dilated convolution is used to avoid the gridding artifacts and a gated subnetwork is applied to fuse the features of different levels. Experiments show that GCANet can obtain much better performance than all the previous state-of-the-art image dehazing methods both qualitatively and quantitatively
We further apply our proposed GCANet to the image deraining task, which also outperforms previous state-of-the-art image deraining methods and demonstrates its generality.
This paper is implemented with Pytorch framework.
Directly put all your test images under one directory. Then run:
python test.py --task [dehaze | derain] --gpu_id [gpu_id] --indir [input directory] --outdir [output directory]For training, please download the training code from https://drive.google.com/file/d/1T7X1HYztbz6S75vTRNtREgGEOI269KDk/view?usp=sharing
You can use our codes for research purpose only. And please cite our paper when you use our codes.
@article{chen2018gated,
title={Gated Context Aggregation Network for Image Dehazing and Deraining},
author={Chen, Dongdong and He, Mingming and Fan, Qingnan and Liao, Jing and Zhang, Liheng and Hou, Dongdong and Yuan, Lu and Hua, Gang},
journal={WACV 2019},
year={2018}
}
If you find any bugs or have any ideas of optimizing these codes, please contact me via cddlyf [at] gmail [dot] com