  Single image de-hazing is a challenging problem, and it is far from solved.
Most current solutions require paired image datasets that include both hazy
images and their corresponding haze-free ground-truth images. However, in
reality, lighting conditions and other factors can produce a range of haze-free
images that can serve as ground truth for a hazy image, and a single ground
truth image cannot capture that range. This limits the scalability and
practicality of paired image datasets in real-world applications. In this
paper, we focus on unpaired single image de-hazing and we do not rely on the
ground truth image or physical scattering model. We reduce the image de-hazing
problem to an image-to-image translation problem and propose a dehazing
Global-Local Cycle-consistent Generative Adversarial Network (Dehaze-GLCGAN).
Generator network of Dehaze-GLCGAN combines an encoder-decoder architecture
with residual blocks to better recover the haze free scene. We also employ a
global-local discriminator structure to deal with spatially varying haze.
Through ablation study, we demonstrate the effectiveness of different factors
in the performance of the proposed network. Our extensive experiments over
three benchmark datasets show that our network outperforms previous work in
terms of PSNR and SSIM while being trained on smaller amount of data compared
to other methods.
