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 this https URL

本研究提出了一种直接恢复最终无雾图像的端到端门控上下文聚合网络方法，采用最新的平滑膨胀技术帮助消除由扩张卷积引起的网格状伪影，并利用门控子网络融合不同层次的特征。广泛的实验表明，所提出的方法在定量和定性上都能超越以前的最先进方法。此外，我们还将所提出的方法应用于图像去雨任务，同样取得了最先进的性能；代码已在 https URL 上公开发表。