GMAN is a awesome Convolutional neural network purposed on haze removal. It is a completely end-to-end dehaze system so the input to the system is hazed rgb images and the output of the system is the clear images that processed by the system. The results can be found in the Results column, where lists our evaluation results on SOTS evaluation dataset. There lists 500 images for indoor test and 500 images for outdoor test saparately, in each file, we can also find a log.txt file showing the psnr and ssim for each images.

1. Step 1:
   • You need to install all packages required by the program, such as tensorflow(GPU version), numpy etc.
   • You need to download current repository into your local environment and extract it.
2. Step 2:
   • Go to your local file and enter the setup.sh file. Change the line 14, "PROGRAM_ROOT_PATH=''" to the path you want.
   • Run sh setup.sh, it will automatically create the directories for running the program

3. Step 3:
   • Move the hazed images to (PROGRAM_ROOT_PATH)/HazeImages/TestImages.
   • If you have ground truth images to compare with the pictures processed by the net, move them to (PROGRAM_ROOT_PATH)/ClearImages/TestImages.
4. Step 4:
   • Copy the model from (DOWNLOAD FILE)/Results/models/(outdoor or indoor) to (PROGRAM_ROOT_PATH)/DeHazeNetModel
5. Step 5:
   • If you want to compare the result with groundtruth, you just need to run the program by using python3 gman_eval.py.
   • If you don't have ground truth, you can can run the program by using python3 gman_eval.py --eval_only_haze=True.
6. Step 6:
   • You results are in (PROGRAM_ROOT_PATH)/ClearResultImages.

Only listed several examples, more results can be found in my github.

Hazy	Groundtruth	Our result

Hazy	Groundtruth	Our result

1. Reference: Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
2. Code work: GMEAN Code
3. Co-worker: Zheng Liu

Please cite the paper if you are using this project.

@article{liu2019single,
  title={Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network},
  author={Liu, Zheng and Xiao, Botao and Alrabeiah, Muhammad and Wang, Keyan and Chen, Jun},
  journal={IEEE Signal Processing Letters},
  volume={26},
  number={6},
  pages={833--837},
  year={2019},
  publisher={IEEE}
}