We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods. Code will be made available at: https://github.com/hezhangsprinter

本文提出了一种新的称为Densely Connected Pyramid Dehazing Network（DCPDN）的端到端单一图像去雾方法，该方法可以联合学习传输图、大气光和去雾。该论文的方法在去雾过程中采用物理学驱动的散射模型，使用新的保边损失函数进行优化，并引入基于生成性对抗网络框架的联合鉴别器以进一步合并估计的传输图和去雾结果间的相互结构信息。实验表明，该方法在去雾效果上取得了显着提高。

密集连接金字塔去雾网络