Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression
文献类型：期刊论文
| 作者 | Xie, Yuan1 ; Zhang, Wensheng1; Tao, Dacheng2; Hu, Wenrui1; Qu, Yanyun3; Wang, Hanzi3; Yuan Xie
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2016-10-01 |
| 卷号 | 25期号:10页码:4943-4958 |
| 关键词 | Image Restoration Atmospheric Turbulence Total Variation Deformation-guided Kernel |
| DOI | 10.1109/TIP.2016.2598638 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | ATMOSPHERIC-TURBULENCE ; INFORMATION FUSION ; IMAGE ; RECONSTRUCTION ; REGULARIZATION ; DECONVOLUTION ; REGISTRATION ; RESTORATION ; ALGORITHMS ; RECOVERY |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000390221100022 |
| 资助机构 | Hong Kong Scholar Program ; National Natural Science Foundation of China(61402480 ; Australian Research Council(DP-120103730 ; 61432008 ; FT-130101457) ; 61472423 ; 61502495 ; 41401383 ; 61373077) |
| 源URL | [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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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