Real-world Image Dehazing Method of MAGNet Fusing Guided Filtering
SANG Yu1,SHEN Hong-qian1,ZHANG Shi-hui1,2,LU Jia-qi1,ZUO Dong-xu1,NIU Jing-chun1,2
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, Hebei 066004, China
Abstract In order to improve the accuracy and applicable scope of the single image dehazing method, a real-world image dehazing method combining multiple attention grid network(MAGNet) and guided filtering is proposed. Firstly, according to the distribution characteristics and imaging principles of haze in real-world images, the multi-attention residual dense block is designed to effectively extract haze-related features in real-world images and the risk of gradient disappearance is reduced. Secondly, the end-to-end convolutional neural network based on the designed multi-attention residual dense block is constructed to remove haze from hazy images. Finally, the guided filtering is introduced into the dehazing problem to enhance the visual effect of dehazing images. The experimental results show that compared with the existing representative image dehazing methods, the proposed method can not only remove haze from real-world images, but also effectively remove haze from synthetic images, and the visual effect of dehazing image is better.
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2022, Vol. 43 
