文献类型：会议论文

英文题名：A Feature Attention Dehazing Network based on U-Net and Dense Connection

作者：Jing, Hongyuan[1,2,3];Zha, Quanxing[2,3];Fu, Yiran[2,3];Lv, Hejun[2,3];Chen, Aidong[1,2,3]

通讯作者：Jing, HY[1];Chen, AD[1];Jing, HY[2];Zha, QX[2];Chen, AD[2];Jing, HY[3];Zha, QX[3];Chen, AD[3]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Multiagent Syst Res Ctr, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Coll Robot, 4 Gongti North Rd, Beijing 10002, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[2]corresponding author), Beijing Union Univ, Multiagent Syst Res Ctr, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[3]corresponding author), Beijing Union Univ, Coll Robot, 4 Gongti North Rd, Beijing 10002, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

会议论文集：13th International Conference on Graphics and Image Processing (ICGIP)

会议日期：AUG 18-20, 2021

会议地点：Yunnan Univ, Kunming, PEOPLES R CHINA

主办单位：Yunnan Univ

语种：英文

外文关键词：Nonhomogeneous Dehazing; FAD-U-Net Structure; Feature Attention; Dense Connection; Residual Dense Block

摘要：The image acquisition equipment influenced by the dense fog, dust, and other weather factors produced low-contrast and low-quality images, which are unfriendly to smart city applications. Various algorithms, models, and networks have been proposed to achieve the dehazing work, all of which have behaved well to the uniform noise. However, in the real scene, due to the nonhomogeneous distribution of the fog, the performances of the dehazing method still need to be improved. In this paper, we proposed a Feature Attention Dehazing Network based on U-Net and Dense Connection, which is FAD-U-Net, to minimize the influence of the nonhomogeneous haze. The FAD-U-Net employed the U-Net as the base structure used the feature attention model to fit the contracting path of U-Net; in addition, the dense connection technique has been added between each block on both contracting path and expansive path, which aims to enhance the feature information. Finally, a group of 18 residual blocks has been added to the networks to improve the image quality. Experiment results show that the proposed dehazing network outperforms most of the existing dehazing methods under nonhomogeneous fogging environments. Regarding the four state-of-art methods we compared in this paper, the PSNR value is increased by 3.21 on average, and SSIM is increased by 0.14. The FAD-U-Net is suitable for most of the application scene in the smart city.