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Journal of Electronic Imaging

Image superresolution by midfrequency sparse representation and total variation regularization
Author(s): Jian Xu; Zhiguo Chang; Jiulun Fan; Xiaoqiang Zhao; Xiaomin Wu; Yanzi Wang
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Paper Abstract

Machine learning has provided many good tools for superresolution, whereas existing methods still need to be improved in many aspects. On one hand, the memory and time cost should be reduced. On the other hand, the step edges of the results obtained by the existing methods are not clear enough. We do the following work. First, we propose a method to extract the midfrequency features for dictionary learning. This method brings the benefit of a reduction of the memory and time complexity without sacrificing the performance. Second, we propose a detailed wiping-off total variation (DWO-TV) regularization model to reconstruct the sharp step edges. This model adds a novel constraint on the downsampling version of the high-resolution image to wipe off the details and artifacts and sharpen the step edges. Finally, step edges produced by the DWO-TV regularization and the details provided by learning are fused. Experimental results show that the proposed method offers a desirable compromise between low time and memory cost and the reconstruction quality.

Paper Details

Date Published: 27 February 2015
PDF: 29 pages
J. Electron. Imag. 24(1) 013039 doi: 10.1117/1.JEI.24.1.013039
Published in: Journal of Electronic Imaging Volume 24, Issue 1
Show Author Affiliations
Jian Xu, Xi'an Jiaotong Univ. (China)
Xi’an Univ. of Posts and Telecommunications (China)
Zhiguo Chang, Chang'an Univ. (China)
Jiulun Fan, Xi'an Univ. of Posts & Telecommunications (China)
Xiaoqiang Zhao, Xi’an Univ. of Posts and Telecommunications (China)
Xiaomin Wu, Xi'an Univ. of Posts & Telecommunications (China)
Yanzi Wang, Xi'an Univ. of Posts & Telecommunications (China)

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