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Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach
Liu, Xin1,2; Zhang, He1,2; Cheung, Yiu-ming3; You, Xinge4; Tang, Yuan Yan5
2017-09
Source PublicationComputer Vision and Image Understanding
ISSN1077-3142
Volume162Pages:23-33
Abstract

Images of outdoor scenes captured in bad weathers are often plagued by the limited visibility and poor contrast, and such degradations are spatially-varying. Differing from most previous dehazing approaches that remove the haze effect in spatial domain and often suffer from the noise problem, this paper presents an efficient multi-scale correlated wavelet approach to solve the image dehazing and denoising problem in the frequency domain. To this end, we have heuristically found a generic regularity in nature images that the haze is typically distributed in the low frequency spectrum of its multi-scale wavelet decomposition. Benefited from this separation, we first propose an open dark channel model (ODCM) to remove the haze effect in the low frequency part. Then, by considering the coefficient relationships between the low frequency and high frequency parts, we employ the soft-thresholding operation to reduce the noise and synchronously utilize the estimated transmission in ODCM to further enhance the texture details in the high frequency parts adaptively. Finally, the haze-free image can be well restored via the wavelet reconstruction of the recovered low frequency part and enhanced high frequency parts correlatively. The proposed approach aims not only to significantly increase the perceptual visibility, but also to preserve more texture details and reduce the noise effect as well. The extensive experiments have shown that the proposed approach yields comparative and even better performance in comparison with the state-of-the-art competing techniques. 

KeywordImage Dehazing Multi-scale Correlated Wavelet Open Dark Channel Model Soft-thresholding
DOIhttps://doi.org/10.1016/j.cviu.2017.08.002
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000412965200002
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
The Source to ArticleWOS
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被引频次[WOS]:9   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorLiu, Xin; Zhang, He; Cheung, Yiu-ming; You, Xinge; Tang, Yuan Yan
Affiliation1.College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China
2.Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen, 361021, China
3.Department of Computer Science and Institute of Research and Continuing Education, Hong Kong Baptist University, Hong Kong SAR, China
4.Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
5.Department of Computer and Information Science, University of Macau, Macau SAR, China
Corresponding Author AffilicationUniversity of Macau
推荐引用方式
GB/T 7714
Liu, Xin,Zhang, He,Cheung, Yiu-ming,et al. Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach[J]. Computer Vision and Image Understanding,2017,162:23-33.
APA Liu, Xin,Zhang, He,Cheung, Yiu-ming,You, Xinge,&Tang, Yuan Yan.(2017).Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach.Computer Vision and Image Understanding,162,23-33.
MLA Liu, Xin,et al."Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach".Computer Vision and Image Understanding 162(2017):23-33.
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