Image superresolution by midfrequency sparse representation and total variation regularization

Jian Xu; Zhiguo Chang; Jiulun Fan; Xiaoqiang Zhao; Xiaomin Wu; Yanzi Wang

doi:10.1117/1.JEI.24.1.013039

27 February 2015 Image superresolution by midfrequency sparse representation and total variation regularization

Jian Xu, Zhiguo Chang, Jiulun Fan, Xiaoqiang Zhao, Xiaomin Wu, Yanzi Wang

Author Affiliations +

Funded by: National Science Foundation of China

Journal of Electronic Imaging, Vol. 24, Issue 1, 013039 (February 2015). https://doi.org/10.1117/1.JEI.24.1.013039

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.

Citation Download Citation

Jian Xu, Zhiguo Chang, Jiulun Fan, Xiaoqiang Zhao, Xiaomin Wu, and Yanzi Wang "Image superresolution by midfrequency sparse representation and total variation regularization," Journal of Electronic Imaging 24(1), 013039 (27 February 2015). https://doi.org/10.1117/1.JEI.24.1.013039

Published: 27 February 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE