  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
