Single image dehazing with an independent Detail-Recovery Network

Abstract

Single image dehazing is a prerequisite that affects the performance of many visually related tasks and has attracted increasing attention in recent years. However, most existing dehazing methods place more emphasis 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 integrating them into a coarse dehazed image. The overall network consists of two independent networks, named DRN and the dehazing network. Specifically, the DRN aims to recover the dehazed image details through the joint efforts of the local branch and the global branch. The local branch can obtain local detail information through the convolution layer, and the global branch can capture multi-scale global information by Smooth Dilated Convolution (SDC). In addition, we apply multi-faceted loss to improve the stability of the dehazing model. Extensive experiments on public image dehazing datasets illustrate the effectiveness of the modules in the proposed method and reveal that our method outperforms state-of-the-art dehazing methods. The code is released in https://github.com/YanLi-LY/Dehazing-DRN.