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Single image super-resolution based on image patch classification
Author(s): Ping Xia; Hua Yan; Jing Li; Jiande Sun
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Paper Abstract

This paper proposed a single image super-resolution algorithm based on image patch classification and sparse representation where gradient information is used to classify image patches into three different classes in order to reflect the difference between the different types of image patches. Compared with other classification algorithms, gradient information based algorithm is simpler and more effective. In this paper, each class is learned to get a corresponding sub-dictionary. High-resolution image patch can be reconstructed by the dictionary and sparse representation coefficients of corresponding class of image patches. The result of the experiments demonstrated that the proposed algorithm has a better effect compared with the other algorithms.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044319 (19 June 2017); doi: 10.1117/12.2280380
Show Author Affiliations
Ping Xia, Shandong Univ. of Finance and Economics (China)
Hua Yan, Shandong Univ. of Finance and Economics (China)
Jing Li, Shandong Management Univ. (China)
Jiande Sun, Shandong Normal Univ. (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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