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Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression
Xie, Yuan1; Zhang, Wensheng1; Tao, Dacheng2; Hu, Wenrui1; Qu, Yanyun3; Wang, Hanzi3; Yuan Xie
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2016-10-01
Volume25Issue:10Pages:4943-4958
SubtypeArticle
AbstractIt remains a challenge to simultaneously remove geometric distortion and space-time-varying blur in frames captured through a turbulent atmospheric medium. To solve, or at least reduce these effects, we propose a new scheme to recover a latent image from observed frames by integrating a new hybrid total variation model and deformation-guided spatial-temporal kernel regression. The proposed scheme first constructs a high-quality reference image from the observed frames using low-rank decomposition. Then, to generate an improved registered sequence, the reference image is iteratively optimized using a variational model containing the combined regularization of local and non-local total variations. The proposed optimization algorithm efficiently solves this model with convergence guarantee. Next, to reduce blur variation, deformation-guided spatial-temporal kernel regression is carried out to fuse the registered sequence into one image by introducing the concept of the near-stationary patch. Applying a blind deconvolution algorithm to the fused image produces the final output. Extensive experimental testing shows, both qualitatively and quantitatively, that the proposed method can effectively alleviate distortion, and blur and recover details of the original scene compared to the state-of-the-art methods.
KeywordImage Restoration Atmospheric Turbulence Total Variation Deformation-guided Kernel
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2016.2598638
WOS KeywordATMOSPHERIC-TURBULENCE ; INFORMATION FUSION ; IMAGE ; RECONSTRUCTION ; REGULARIZATION ; DECONVOLUTION ; REGISTRATION ; RESTORATION ; ALGORITHMS ; RECOVERY
Indexed BySCI
Language英语
Funding OrganizationHong Kong Scholar Program ; National Natural Science Foundation of China(61402480 ; Australian Research Council(DP-120103730 ; 61432008 ; FT-130101457) ; 61472423 ; 61502495 ; 41401383 ; 61373077)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000390221100022
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12258
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorYuan Xie
Affiliation1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
3.Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China
Recommended Citation
GB/T 7714
Xie, Yuan,Zhang, Wensheng,Tao, Dacheng,et al. Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(10):4943-4958.
APA Xie, Yuan.,Zhang, Wensheng.,Tao, Dacheng.,Hu, Wenrui.,Qu, Yanyun.,...&Yuan Xie.(2016).Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(10),4943-4958.
MLA Xie, Yuan,et al."Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.10(2016):4943-4958.
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