Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/653092
Title: Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression
Authors: Yuan Xie;Wensheng Zhang;Dacheng Tao;Wenrui Hu;Yanyun Qu;Hanzi Wang
subject: total variation|deformation-guided kernel|atmospheric turbulence|Image restoration
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/160020
http://localhost/handle/Hannan/653092
ISSN: 1057-7149
1941-0042
volume: 25
issue: 10
Appears in Collections:2016

Files in This Item:
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7536179.pdf9.19 MBAdobe PDFThumbnail
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Title: Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression
Authors: Yuan Xie;Wensheng Zhang;Dacheng Tao;Wenrui Hu;Yanyun Qu;Hanzi Wang
subject: total variation|deformation-guided kernel|atmospheric turbulence|Image restoration
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/160020
http://localhost/handle/Hannan/653092
ISSN: 1057-7149
1941-0042
volume: 25
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7536179.pdf9.19 MBAdobe PDFThumbnail
Preview File
Title: Removing Turbulence Effect via Hybrid Total Variation and Deformation-Guided Kernel Regression
Authors: Yuan Xie;Wensheng Zhang;Dacheng Tao;Wenrui Hu;Yanyun Qu;Hanzi Wang
subject: total variation|deformation-guided kernel|atmospheric turbulence|Image restoration
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/160020
http://localhost/handle/Hannan/653092
ISSN: 1057-7149
1941-0042
volume: 25
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7536179.pdf9.19 MBAdobe PDFThumbnail
Preview File