dc.contributor.author | Xie, Liangru | |
dc.contributor.author | Wang, Hao | |
dc.contributor.author | Wang, Zhuowei | |
dc.contributor.author | Cheng, Lianglun | |
dc.date.accessioned | 2020-10-22T12:39:44Z | |
dc.date.available | 2020-10-22T12:39:44Z | |
dc.date.created | 2020-10-03T14:48:15Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-1-7281-6926-2 | |
dc.identifier.uri | https://hdl.handle.net/11250/2684521 | |
dc.description.abstract | Eliminating haze interference in images is still a challenging problem. In this paper, we consider more systematically the physical hazing mechanisms, combined with deep learning, propose a new end-to-end dehazing network called DHD-Net. For physical hazing mechanisms, we fuse the global atmosphere light, transmission maps, and the atmospheric scattering model for dehazing. For the estimation of global atmosphere light, We propose a deep learning-based haze density estimation algorithm (DL-HDE). We establish a new dataset, of which each data item consists of the hazy image, the transmission map, the haze-free image, and the dense-haze area mask. Our experimental results demonstrate that our proposed DHD-Net has better dehazing performance than state-of-the-art algorithms. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN 2020) | |
dc.title | DHD-Net: A Novel Deep-Learning-based Dehazing Network | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.identifier.doi | 10.1109/IJCNN48605.2020.9207316 | |
dc.identifier.cristin | 1836783 | |
dc.description.localcode | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |