Densely Connected Pyramid Dehazing Network

Abstract

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 incor- We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Net- work (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 atmo- spheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense net- work 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 transmis- sion map. This network is optimized using a newly in- troduced edge-preserving loss function. To further incor- porate the mutual structural information between the esti- mated transmission map and the dehazed result, we pro- pose a joint-discriminator based on generative adversar- ial network framework to decide whether the correspond- ing dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demon- strate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Exten- sive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the- art methods. Code and dataset is made available at: https://github.com/hezhangsprinter/DCPDN