• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Hierarchical Regression for Single Image Super Resolution via Clustering and Sparse Representation

Vol. 11, No.5, May 31, 2017
10.3837/tiis.2017.05.013, Download Paper (Free):

Abstract

Regression-based image super resolution (SR) methods have shown great advantage in time consumption while maintaining similar or improved quality performance compared to other learning-based methods. In this paper, we propose a novel single image SR method based on hierarchical regression to further improve the quality performance. As an improvement to other regression-based methods, we introduce a hierarchical scheme into the process of learning multiple regressors. First, training samples are grouped into different clusters according to their geometry similarity, which generates the structure layer. Then in each cluster, a compact dictionary can be learned by Sparse Coding (SC) method and the training samples can be further grouped by dictionary atoms to form the detail layer. Last, a series of projection matrixes, which anchored to dictionary atoms, can be learned by linear regression. Experiment results show that hierarchical scheme can lead to regression that is more precise. Our method achieves superior high quality results compared with several state-of-the-art methods.


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Cite this article

[IEEE Style]
Kang Qiu, Benshun Yi, Weizhong Li and Taiqi Huang, "Hierarchical Regression for Single Image Super Resolution via Clustering and Sparse Representation," KSII Transactions on Internet and Information Systems, vol. 11, no. 5, pp. 2539-2554, 2017. DOI: 10.3837/tiis.2017.05.013

[ACM Style]
Qiu, K., Yi, B., Li, W., and Huang, T. 2017. Hierarchical Regression for Single Image Super Resolution via Clustering and Sparse Representation. KSII Transactions on Internet and Information Systems, 11, 5, (2017), 2539-2554. DOI: 10.3837/tiis.2017.05.013