Browse > Article
http://dx.doi.org/10.9728/dcs.2017.18.8.1517

Super Resolution Algorithm using TV-G Decomposition  

Eum, Kyoung-Bae (Department of Computer and Information Eng., Kunsan National University,)
Beom, Dong-Kyu (Department of Computer and Information Eng., Kunsan National University,)
Publication Information
Journal of Digital Contents Society / v.18, no.8, 2017 , pp. 1517-1522 More about this Journal
Abstract
Among single image SR techniques, the TV based SR approach seems most successful in terms of edge preservation and no artifacts. But, this approach achieves insufficient SR for texture component. In this paper, we proposed a new TV-G decomposition based SR method to solve this problem. We proposed the SVR based up-sampling to get better edge preservation in the structure component. The NNE used the relaxed constraint to improve the NE. We used the NNE based learning method to improve the resolution of the texture component. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed SR method when comparing with conventional interpolation method, ScSR, TV and NNE.
Keywords
Super Resolution; TV-G Decomposition; Structure Image; Texture; NNE;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 R. S. Wagner, D. E. Waagen, and M. L. Cassabaum, "Image super resolution for improved automatic target recognition," in Proceeding of the SPIE, Vol. 5426, 2004.
2 D. Li and Steven Simske, "Example based single frame image super resolution by support vector regression," Patten Recognition Research, Vol. 5, No. 1, 2010.
3 H. Chang, D. Yeung, and Y. Xiong, "Super resolution through neighbor embedding," in Proceeding of the IEEE CVPR, Vol. 1, 2004.
4 Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi Morel, "Low Complexity Single Image Super Resolution based on Nonnegative Neighbor Embedding," in Proceeding of the BMVC, 2012.
5 Y. Sakuta, A. Tsutsui, T. Goto, M. Sakurai and R. Sakai, "Super Resolution utilizing Total Variation Regularization on CELL Processor," in Proceeding of the International Conference on Consumer Electronics, 2012.
6 Jianchao Yang, J. Wright, T.S. Huang, and Yi Ma., "Image Super Resolution Via Sparse Representation," IEEE Trans. on Image Processing, Vol. 19, No. 11, 2010.
7 J. F. Aujol, G. Gilboa, T. Chan, and S. Osher., Structure texture image decomposition - modeling, algorithms, and parameter selection, UCLA, CAM Report 05-10, 2005.
8 D. Gabor., "Theory of communication," Journal Inst. of Electrical Engineering, Vol. 93, No. 3, 1946.
9 J. F. Aujol, G. Gilboa, T. Chan, and S. Osher., Structure Texture Decomposition by a TV-Garbor Model, UCLA, CAM Report 05-11, 2005.
10 V. Vapnik, Statistical Learning Theory, Wiley Interscience, 1998.
11 C. Chang and C. Lin, "LIBSVM : a library for support vector machines," 2001.
12 J. W. Kim and Y. S. Choi, "A Steganography Method Improving Image Quality and Minimizing Image Degradation," Journal of DCS, Vol. 17, No. 5, 2016.