  Image dehazing aims to recover the uncorrupted content from a hazy image.
Instead of leveraging traditional low-level or handcrafted image priors as the
restoration constraints, e.g., dark channels and increased contrast, we propose
an end-to-end gated context aggregation network to directly restore the final
haze-free image. In this network, we adopt the latest smoothed dilation
technique to help remove the gridding artifacts caused by the widely-used
dilated convolution with negligible extra parameters, and leverage a gated
sub-network to fuse the features from different levels. Extensive experiments
demonstrate that our method can surpass previous state-of-the-art methods by a
large margin both quantitatively and qualitatively. In addition, to demonstrate
the generality of the proposed method, we further apply it to the image
deraining task, which also achieves the state-of-the-art performance. Code has
been made available at https://github.com/cddlyf/GCANet.
