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

Sparse Representation based Two-dimensional Bar Code Image Super-resolution

Vol. 11, No.4, April 30, 2017
10.3837/tiis.2017.04.016, Download Paper (Free):

Abstract

This paper presents a super-resolution reconstruction method based on sparse representation for two-dimensional bar code images. Considering the features of two-dimensional bar code images, Kirsch and LBP (local binary pattern) operators are used to extract the edge gradient and texture features. Feature extraction is constituted based on these two features and additional two second-order derivatives. By joint dictionary learning of the low-resolution and high-resolution image patch pairs, the sparse representation of corresponding patches is the same. In addition, the global constraint is exerted on the initial estimation of high-resolution image which makes the reconstructed result closer to the real one. The experimental results demonstrate the effectiveness of the proposed algorithm for two-dimensional bar code images by comparing with other reconstruction algorithms.


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Cite this article

[IEEE Style]
Yiling Shen, Ningzhong Liu and Han Sun, "Sparse Representation based Two-dimensional Bar Code Image Super-resolution," KSII Transactions on Internet and Information Systems, vol. 11, no. 4, pp. 2109-2123, 2017. DOI: 10.3837/tiis.2017.04.016

[ACM Style]
Shen, Y., Liu, N., and Sun, H. 2017. Sparse Representation based Two-dimensional Bar Code Image Super-resolution. KSII Transactions on Internet and Information Systems, 11, 4, (2017), 2109-2123. DOI: 10.3837/tiis.2017.04.016