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BiN-Flow: Bidirectional Normalizing Flow for Robust Image Dehazing

文献类型：期刊论文

作者Wu, Yiqiang1,5; Tao, Dapeng3,5; Zhan, Yibing4; Zhang, Chenyang2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2022
卷号31页码:6635-6648
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
ISSN号1057-7149
DOI10.1109/TIP.2022.3214093
通讯作者Tao, Dapeng(dptao@ynu.edu.cn)
英文摘要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.
WOS关键词RESTORATION ; VISIBILITY ; NETWORK ; WEATHER
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]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000873807400005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Yunnan Provincial Major Science and Technology Special Plan Projects ; National Natural Science Foundation of China ; Yunnan Natural Science Funds
源URL[http://ir.ia.ac.cn/handle/173211/50514]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Tao, Dapeng
作者单位1.Yunnan Key Lab Intelligent Syst & Comp, Kunming 650500, Yunnan, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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.Yunnan Univ, Sch Informat Sci & Engn, FIST Lab, Kunming 650500, Yunnan, 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.

入库方式： OAI收割

来源：自动化研究所

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