BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing

doi:10.1109/TIP.2022.3214093

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing
	Wu, Yiqiang 1,2; Tao, Dapeng 1,3; Zhan, Yibing 4; Zhang, Chenyang5
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2022
卷号	31 页码:6635-6648
通讯作者	Tao, Dapeng(dptao@ynu.edu.cn)
摘要	Image dehazing aims to remove haze in images to improve their image quality. However, most image dehazing methods heavily depend on strict prior knowledge and paired training strategy, which would hinder generalization and performance when dealing with unseen scenes. In this paper, to address the above problem, we propose Bidirectional Normalizing Flow (BiN-Flow), which exploits no prior knowledge and constructs a neural network through weakly-paired training with better generalization for image dehazing. Specifically, BiN-Flow designs 1) Feature Frequency Decoupling (FFD) for mining the various texture details through multi-scale residual blocks and 2) Bidirectional Propagation Flow (BPF) for exploiting the one-to-many relationships between hazy and haze-free images using a sequence of invertible Flow. In addition, BiN-Flow constructs a reference mechanism (RM) that uses a small number of paired hazy and haze-free images and a large number of haze-free reference images for weakly-paired training. Essentially, the mutual relationships between hazy and haze-free images could be effectively learned to further improve the generalization and performance for image dehazing. We conduct extensive experiments on five commonly-used datasets to validate the BiN-Flow. The experimental results that BiN-Flow outperforms all state-of-the-art competitors demonstrate the capability and generalization of our BiN-Flow. Besides, our BiN-Flow could produce diverse dehazing images for the same image by considering restoration diversity.
关键词	Training Image restoration Image color analysis Band-pass filters Convolutional neural networks Atmospheric modeling Noise measurement Image dehazing bidirectional normalizing flow weakly-paired training and restoration diversity
DOI	10.1109/TIP.2022.3214093
关键词[WOS]	RESTORATION ; VISIBILITY ; NETWORK ; WEATHER
收录类别	SCI
语种	英语
资助项目	Yunnan Provincial Major Science and Technology Special Plan Projects[202202AD080003] ; National Natural Science Foundation of China[62172354] ; National Natural Science Foundation of China[62002090] ; National Natural Science Foundation of China[62206293] ; Yunnan Natural Science Funds[202101AS070047] ; Yunnan Natural Science Funds[202205AG070003] ; Yunnan Natural Science Funds[2019FA-045]
项目资助者	Yunnan Provincial Major Science and Technology Special Plan Projects ; National Natural Science Foundation of China ; Yunnan Natural Science Funds
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000873807400005
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50515
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Tao, Dapeng
作者单位	1.Yunnan Univ, Sch Informat Sci & Engn, FIST Lab, Kunming 650500, Yunnan, Peoples R China 2.Yunnan Key Lab Intelligent Syst & Comp, Kunming 650500, Yunnan, Peoples R China 3.Yunnan United Vis Technol Co Ltd, Kunming 650500, Yunnan, Peoples R China 4.JD Explore Acad, Beijing 100000, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wu, Yiqiang,Tao, Dapeng,Zhan, Yibing,et al. BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:6635-6648.
APA	Wu, Yiqiang,Tao, Dapeng,Zhan, Yibing,&Zhang, Chenyang.(2022).BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,6635-6648.
MLA	Wu, Yiqiang,et al."BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):6635-6648.