  Single image dehazing is a prerequisite which affects the performance of many
computer vision tasks and has attracted increasing attention in recent years.
However, most existing dehazing methods emphasize more on haze removal but less
on the detail recovery of the dehazed images. In this paper, we propose a
single image dehazing method with an independent Detail Recovery Network (DRN),
which considers capturing the details from the input image over a separate
network and then integrates them into a coarse dehazed image. The overall
network consists of two independent networks, named DRN and the dehazing
network respectively. Specifically, the DRN aims to recover the dehazed image
details through local and global branches respectively. The local branch can
obtain local detail information through the convolution layer and the global
branch can capture more global information by the Smooth Dilated Convolution
(SDC). The detail feature map is fused into the coarse dehazed image to obtain
the dehazed image with rich image details. Besides, we integrate the DRN, the
physical-model-based dehazing network and the reconstruction loss into an
end-to-end joint learning framework. Extensive experiments on the public image
dehazing datasets (RESIDE-Indoor, RESIDE-Outdoor and the TrainA-TestA)
illustrate the effectiveness of the modules in the proposed method and show
that our method outperforms the state-of-the-art dehazing methods both
quantitatively and qualitatively. The code is released in
https://github.com/YanLi-LY/Dehazing-DRN.
