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Image superresolution by midfrequency sparse representation and total variation regularization

[+] Author Affiliations
Jian Xu

Xi’an Jiaotong University, Image Processing and Recognition Center, Xianning Road, Xi’an 710049, China

Xi’an University of Posts and Telecommunications, School of Telecommunication and Information Engineering, Weiguo Road, Xi’an 710121, China

Zhiguo Chang

Chang’an University, School of Information Engineering, Erhuan Road, Xi’an 710064, China

Jiulun Fan, Xiaoqiang Zhao, Xiaomin Wu, Yanzi Wang

Xi’an University of Posts and Telecommunications, School of Telecommunication and Information Engineering, Weiguo Road, Xi’an 710121, China

J. Electron. Imaging. 24(1), 013039 (Feb 27, 2015). doi:10.1117/1.JEI.24.1.013039
History: Received September 24, 2014; Accepted January 23, 2015
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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.

© 2015 SPIE and IS&T

Citation

Jian Xu ; Zhiguo Chang ; Jiulun Fan ; Xiaoqiang Zhao ; Xiaomin Wu, et al.
"Image superresolution by midfrequency sparse representation and total variation regularization", J. Electron. Imaging. 24(1), 013039 (Feb 27, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013039


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