  Model-based single image dehazing algorithms restore images with sharp edges
and rich details at the expense of low PSNR values. Data-driven ones restore
images with high PSNR values but with low contrast, and even some remaining
haze. In this paper, a novel single image dehazing algorithm is introduced by
fusing model-based and data-driven approaches. Both transmission map and
atmospheric light are initialized by the model-based methods, and refined by
deep learning approaches which form a neural augmentation. Haze-free images are
restored by using the transmission map and atmospheric light. Experimental
results indicate that the proposed algorithm can remove haze well from
real-world and synthetic hazy images.

Computer Vision and Pattern Recognition