  Model-based single image dehazing algorithms restore haze-free images with
sharp edges and rich details for real-world hazy images at the expense of low
PSNR and SSIM values for synthetic hazy images. Data-driven ones restore
haze-free images with high PSNR and SSIM values for synthetic hazy images but
with low contrast, and even some remaining haze for real world hazy images. In
this paper, a novel single image dehazing algorithm is introduced by combining
model-based and data-driven approaches. Both transmission map and atmospheric
light are first estimated by the model-based methods, and then refined by
dual-scale generative adversarial networks (GANs) based approaches. The
resultant algorithm forms a neural augmentation which converges very fast while
the corresponding data-driven approach might not converge. Haze-free images are
restored by using the estimated transmission map and atmospheric light as well
as the Koschmiederlaw. Experimental results indicate that the proposed
algorithm can remove haze well from real-world and synthetic hazy images.
