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
2016-10-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
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
DOI10.1109/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12258
专题精密感知与控制研究中心_人工智能与机器学习
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
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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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