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Journal of Electronic Imaging

Single-image super-resolution using directional total variation regularization and alternating direction method of multiplier solver
Author(s): Qiang Wang; Zhenghua Wu; Mingjian Sun; Ting Liu; Bo Li; Naizhang Feng; Yi Shen
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Paper Abstract

Single-image super-resolution (SR) is one of the most important and challenging issues in image processing. To produce a high-resolution image from a low-resolution image, one of the conventional approaches is to leverage regularization to overcome the limitations caused by the modeling. However, conventional regularizers such as total variation always neglect the high-level structures in the data. To overcome the drawback, we propose to explore the underlying information for the images with structured edges by using directional total variation. An alternating direction method of a multiplier-based algorithm is presented to effectively solve the resulting optimization problem. Computer simulations on several texture images such as a leaf image have been used to demonstrate the effectiveness and improvement of the proposed method on SR reconstruction, both qualitatively and quantitatively. Furthermore, the effect of parameter selection is also discussed for the proposed method.

Paper Details

Date Published: 7 April 2015
PDF: 9 pages
J. Electron. Imaging. 24(2) 023026 doi: 10.1117/1.JEI.24.2.023026
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Qiang Wang, Harbin Institute of Technology (China)
Zhenghua Wu, Harbin Institute of Technology (China)
Mingjian Sun, Harbin Institute of Technology (China)
Ting Liu, Harbin Institute of Technology (China)
Bo Li, Harbin Institute of Technology (China)
Naizhang Feng, Harbin Institute of Technology (China)
Yi Shen, Harbin Institute of Technology (China)


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