Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression

doi:10.1109/TIP.2016.2598638

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression
	Xie, Yuan1 ; Zhang, Wensheng1 ; Tao, Dacheng 2; Hu, Wenrui1 ; Qu, Yanyun 3; Wang, Hanzi 3; Yuan Xie
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2016-10-01
卷号	25 期号:10 页码:4943-4958
文章类型	Article
摘要	It 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.
关键词	Image Restoration Atmospheric Turbulence Total Variation Deformation-guided Kernel
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2016.2598638
关键词[WOS]	ATMOSPHERIC-TURBULENCE ; INFORMATION FUSION ; IMAGE ; RECONSTRUCTION ; REGULARIZATION ; DECONVOLUTION ; REGISTRATION ; RESTORATION ; ALGORITHMS ; RECOVERY
收录类别	SCI
语种	英语
项目资助者	Hong Kong Scholar Program ; National Natural Science Foundation of China(61402480 ; Australian Research Council(DP-120103730 ; 61432008 ; FT-130101457) ; 61472423 ; 61502495 ; 41401383 ; 61373077)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000390221100022
引用统计	被引频次：40[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/173211/12258
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Yuan Xie
作者单位	1.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
第一作者单位	精密感知与控制研究中心
推荐引用方式 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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