LAUSR

The learning-based methods have recently shown their advantages in the image dehazing task. However, most existing learning-based methods do not pay much attention to the restoration in the edges of the hazy image, resulting in the edge blur of the dehazing results. To mitigate this issue, in this letter, we propose a novel Edge Aware Network (EA-Net) for image dehazing, which can simultaneously model edge features and contextual features into a single network for restoring haze-free image with sharp edges. Firstly, we extract the multi-scales contextual features of hazy image by a progressive fusion way. Furthmore, the abundant edge features are inferred by a edge subnetwork with Edge Feature Extraction Module(EFEM). Finally, we present an Edge Attention (EA) mechanism to couple the edge features with contextual features at various resolutions for sufficiently leveraging these complementary features. Due to the rich edge information and feature fusion strategy, the fused features can make the haze-free image to be clearer, especially at the edges, which is very important for the high-level vision tasks. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.