Region Adaptive Two-Shot Network For Single Image Dehazing

Existing single image dehazing methods typically adopt a one-shot strategy by indiscriminately applying the same filters to all local regions, which easily cause under-/over-dehazing across different regions by ignoring the inhomogeneity and asymmetry of illumination and detail distortions. In this paper, we propose a region adaptive two-shot network (RATNet) to address this issue. In the first shot, a lightweight subnetwork is utilized to conduct the regular global filtering, which could remove parts of haze but also distort some image details. In the second shot, a two-branch subnetwork is developed to restore the illumination and details of the initially renovated image respectively. The final dehazed image is obtained by fusing the outputs of the previous two branches, whose region-variant weights are adaptively learned by minimizing the difference between the haze-free image and our fused result. Experiments on four dehazing benchmark datasets show that our RATNet significantly outperforms many state-of-the-art dehazing approaches.