Non-homogeneous haze data synthesis based real-world image dehazing with enhancement-and-restoration fused CNNs

Single image dehazing is a challenging ill-posed task, we address it from the aspects of dehazing dataset and network architecture. As for the dehazing dataset, there are such problems as unnatural hazy images, unqualified ground truths, as well as monotonous and idealized depth-related haze synthesized by the physical model in the existing dehazing datasets. Therefore, we propose a novel haze data synthesis method to produce a dehazing dataset with non-homogeneous haze, which is named as FiveK-Haze. As for the network architecture, existing methods either get the image enhancement results directly in an end-to-end approach or restore the haze-free images based on the estimated physical parameters in a multiple-branch approach. To get better results for the real-world non-homogeneous haze images, we combine above two approaches together in a complementary way and design a new dehazing network with the enhancement-and-restoration fused CNNs, which is called as ERFNet. Exhaustive experimental results demonstrate the superiority of our method over the state-of-the-art methods in terms of both generalization performance and haze removal effects, especially for detail enhancement and color restoration.