Single-image dehazing is an ill-posed problem. Most previous methods focused on estimating intermediate parameters for input hazy images. In this paper, we propose a novel end-to-end Simplified Non-locally Dense Network (SNDN) which does not rely on intermediate parameters. To capture long-range dependencies, we propose a Simplified Non-local Dense Block (SNDB) which is lightweight and outperforms traditional non-local method. Our SNDB will be embedded into a densely connected encoder–decoder network. To avoid gradients vanishing problem, we propose a simple branch network which only have five convolution layers. The effectiveness of our proposed network is proved through ablation experiment. In addition, we enhanced our training set by synthesizing colored hazy images, which helps restore the original color of the hazy image. The experimental results demonstrate that our network have better performance than most of the pervious state-of-the-art methods.

中文翻译：

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用于单图像去雾的简化非局部密集网络

单图像去雾是一个不适定的问题。以前的大多数方法都侧重于估计输入模糊图像的中间参数。在本文中，我们提出了一种新颖的端到端简化非局部密集网络（SNDN），它不依赖于中间参数。为了捕获远程依赖性，我们提出了一种轻量级且优于传统非局部方法的简化非局部密集块（SNDB）。我们的 SNDB 将嵌入到一个密集连接的编码器-解码器网络中。为了避免梯度消失问题，我们提出了一个只有五个卷积层的简单分支网络。通过消融实验证明了我们提出的网络的有效性。此外，我们通过合成彩色朦胧图像来增强我们的训练集，这有助于恢复朦胧图像的原始颜色。