文献类型：期刊文献

英文题名：Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion

作者：Ma, Shaojin[1];Pan, Weiguo[1];Liu, Hongzhe[1];Dai, Songyin[1];Xu, Bingxin[1];Xu, Cheng[1];Li, Xuewei[2];Guan, Huaiguang[3]

第一作者：Ma, Shaojin

通讯作者：Pan, WG[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China;[3]CATARC Tianjin Automot Engn Res Inst Co Ltd, Tianjin 300300, 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, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

年份：2023

卷号：2023

外文期刊名：ADVANCES IN MULTIMEDIA

收录：EI(收录号：20232314179395);WOS:【ESCI(收录号:WOS:000994945700001)】；

基金：AcknowledgmentsThis work was supported by the Beijing Natural Science Foundation (4232026), National Natural Science Foundation of China (grant nos. 62272049, 62171042, 61871039, 62102033, and 62006020), Key Project of Science and Technology Plan of Beijing Education Commission (KZ202211417048), the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions (no. BPHR20220121), the Collaborative Innovation Center of Chaoyang (no. CYXC2203), and Scientific Research Projects of Beijing Union University (grant nos. ZK10202202, BPHR2020DZ02, ZK40202101, and ZK120202104).

语种：英文

外文关键词：Color - Color image processing - Demulsification - Image enhancement - K-means clustering

摘要：Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disorder, and other challenges; in order to solve these problems, in this paper, an effective image dehazing method is proposed, which is based on improved color channel transfer and multiexposure image fusion to achieve image dehazing. First, the image is preprocessed using a color channel transfer method based on k-means. Second, gamma correction is introduced on the basis of guided filtering to obtain a series of multiexposure images, and the obtained multiexposure images are fused into a dehazed image through a Laplacian pyramid fusion scheme based on local similarity of adaptive weights. Finally, contrast and saturation corrections are performed on the dehazed image. Experimental verification is carried out on synthetic dehazed images and natural dehazed images, and it is verified that the method proposed is superior to existing dehazed algorithms from both subjective and objective aspects.