Hierarchical Density-Aware Dehazing Network
提出一种分层密度感知去雾网络，通过密度生成器和拉普拉斯金字塔解码器，利用雾密度信息引导去雾，并引入多尺度判别器保持全局与局部一致性，在自然和合成雾图上表现优于现有方法。
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.