Single image super-resolution based on image patch classification

Ping Xia; Hua Yan; Jing Li; Jiande Sun

doi:10.1117/12.2280380

19 June 2017 Single image super-resolution based on image patch classification

Ping Xia, Hua Yan, Jing Li, Jiande Sun

Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044319 (2017) https://doi.org/10.1117/12.2280380
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore

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.

Citation Download Citation

Ping Xia, Hua Yan, Jing Li, and Jiande Sun "Single image super-resolution based on image patch classification", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044319 (19 June 2017); https://doi.org/10.1117/12.2280380

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