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

Single Image Dehazing with An Independent Detail-Recovery Network