The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder. The proposed network incorporates density estimation but alleviates the constraint of the atmospheric model. The predicted haze density then guides the Laplacian pyramid decoder to generate a haze-free image in a coarse-to-fine fashion. In addition, we introduce a multiscale discriminator to preserve global and local consistency for dehazing. We conduct extensive experiments on natural and synthetic hazy images, which prove that the proposed model performs favorably against the state-of-the-art dehazing approaches.

中文翻译：

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分层密度感知去雾网络

图像去雾中常用的大气模型在实际情况下无法成立。尽管通过忽略物理模型提出了深度端到端网络来解决这个问题，但大气模型中的传输图包含重要的雾度密度信息，不能简单地忽略。在本文中，我们提出了一种新颖的分层密度感知去雾网络，该网络由密集连接的金字塔编码器、密度生成器和拉普拉斯金字塔解码器组成。所提出的网络结合了密度估计，但减轻了大气模型的约束。然后，预测的雾度密度引导拉普拉斯金字塔解码器以粗到细的方式生成无雾图像。此外，我们引入了一个多尺度鉴别器来保持全局和局部一致性以进行去雾。