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Proceedings Paper

Spatially adapted second-order total generalized variational image deblurring model under impulse noise
Author(s): Qiu-Xiang Zhong; Chuan-Sheng Wu; Qiao-Ling Shu; Ryan Wen Liu
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Paper Abstract

Image deblurring under impulse noise is a typical ill-posed problem which requires regularization methods to guarantee high-quality imaging. L1-norm data-fidelity term and total variation (TV) regularizer have been combined to contribute the popular regularization method. However, the TV-regularized variational image deblurring model often suffers from the staircase-like artifacts leading to image quality degradation. To enhance image quality, the detailpreserving total generalized variation (TGV) was introduced to replace TV to eliminate the undesirable artifacts. The resulting nonconvex optimization problem was effectively solved using the alternating direction method of multipliers (ADMM). In addition, an automatic method for selecting spatially adapted regularization parameters was proposed to further improve deblurring performance. Our proposed image deblurring framework is able to remove blurring and impulse noise effects while maintaining the image edge details. Comprehensive experiments have been conducted to demonstrate the superior performance of our proposed method over several state-of-the-art image deblurring methods.

Paper Details

Date Published: 10 April 2018
PDF: 11 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152L (10 April 2018); doi: 10.1117/12.2303403
Show Author Affiliations
Qiu-Xiang Zhong, Wuhan Univ. of Technology (China)
Chuan-Sheng Wu, Wuhan Univ. of Technology (China)
Qiao-Ling Shu, Wuhan Univ. of Technology (China)
Ryan Wen Liu, Wuhan Univ. of Technology (China)
Wuhan Institute of Technology (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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