Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement

doi:10.1109/TCSVT.2021.3130158

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 遥感图像处理团队

	Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement
	Zhang, Chi 6; Lin, Zihang 6; Xu, Liheng 6; Li, Zongliang 6; Tang, Wei 5; Liu, Yuehu 6; Meng, Gaofeng 2,3,4; Wang, Le 6; Li, Li 1
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2022-07-01
卷号	32 期号:7 页码:4552-4572
关键词	Feature extraction Image synthesis Scattering Generative adversarial networks Atmospheric modeling Training Testing Haze synthesis unsupervised image-to-image translation self-supervised disentanglement
ISSN号	1051-8215
DOI	10.1109/TCSVT.2021.3130158
通讯作者	Liu, Yuehu(liuyh@xjtu.edu.cn)
英文摘要	The key procedure of haze image synthesis with adversarial training lies in the disentanglement of the feature involved only in haze synthesis, i.e., the style feature, from the feature representing the invariant semantic content, i.e., the content feature. Previous methods introduced a binary classifier to constrain the domain membership from being distinguished through the learned content feature during the training stage, thereby the style information is separated from the content feature. However, we find that these methods cannot achieve complete content-style disentanglement. The entanglement of the flawed style feature with content information inevitably leads to the inferior rendering of haze images. To address this issue, we propose a self-supervised style regression model with stochastic linear interpolation that can suppress the content information in the style feature. Ablative experiments demonstrate the disentangling completeness and its superiority in density-aware haze image synthesis. Moreover, the synthesized haze data are applied to test the generalization ability of vehicle detectors. Further study on the relation between haze density and detection performance shows that haze has an obvious impact on the generalization ability of vehicle detectors and that the degree of performance degradation is linearly correlated to the haze density, which in turn validates the effectiveness of the proposed method.
资助项目	National Key Research and Development Project of New Generation Artificial Intelligence of China[2018AAA0102504] ; National Natural Science Foundation of China[61973245]
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000819817700037
资助机构	National Key Research and Development Project of New Generation Artificial Intelligence of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49194]
专题	自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者	Liu, Yuehu
作者单位	1.Tsinghua Univ, Dept Automat, BNRist, Beijing 100084, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, HK Inst Sci & Innovat, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 5.Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA 6.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Chi,Lin, Zihang,Xu, Liheng,et al. Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(7):4552-4572.
APA	Zhang, Chi.,Lin, Zihang.,Xu, Liheng.,Li, Zongliang.,Tang, Wei.,...&Li, Li.(2022).Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(7),4552-4572.
MLA	Zhang, Chi,et al."Density-Aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.7(2022):4552-4572.