FD-GAN

FD-GAN: Generative adversarial Networks with Fusion-discriminator for Single Image Dehazing(AAAI'20)

Yu Dong. Yihao Liu

In this paper, we propose a fully end-to-end algorithm FD-GAN for image dehazing. Moreover, we develop a novel Fusion-discriminator which can integrate the frequency information as additional priors and constraints into the dehazing network. Our method can generate more visually pleasing dehazed results with less color distortion. Extensive experimental results have demonstrated that our method performs favorably against several state-of-the-art methods on both synthetic datasets and real-world hazy images.

Prerequisites

1. Ubuntu 18.04
2. Python 3
3. NVIDIA GPU + CUDA CuDNN (CUDA 8.0)

Installation

1. conda install pytorch=0.3.0 torchvision cuda80 -c pytorch
2. Install python package:numpy,scipy,PIL,skimage

Demo using pre-trained model

Since the proposed method uses hdf5 file to load the traning samples, the generate_testsample.py helps you to creat the testing or training sample yourself.

If your images are real:

python demo.py --dataroot ./facades/'your_folder_name' --netG ./testmodel/netG_epoch_real.pth

If your images are synthetic:

python demo.py --dataroot ./facades/'your_folder_name' --netG ./testmodel/netG_epoch_synthetic.pth

To obtain the best performance on synthetic and real-world datasets respectively, we provide two models from different iterations in one training procedure. In addition, please use netG.train() for testing since the batch for training is 1.

Pre-trained dehazing models can be downloaded at (put it in the folder 'test_model'):

Metric

You can run the PSNRSSIM.py for quantitative results

python PSNRSSIM.py --gt_dir ./your_folder_name --result_dir ./your_folder_name

Datasets

We release our training datast(derived from Micosoft COCO)、RESIDE-SOTS and NTIRE dataset(hdf5 file) as following URL：

How to read the Hdf5 file

Following are the sample python codes how to read the Hdf5 file:

import matplotlib.pyplot as plt
file_name=self.root+'/'+str(index)+'.h5'
f=h5py.File(file_name,'r')

gt=f['gt'][:]
haze=f['haze'][:]
plt.subplot(1,2,1), plt.title('gt')
plt.imshow(gt)
plt.subplot(1,2,2),plt.title('haze')
plt.imshow(haze)
plt.show()

Citation

Acknowledgments

Thank all co-authors deeply!