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http://dx.doi.org/10.3837/tiis.2017.04.016

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

Shen, Yiling (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics)
Liu, Ningzhong (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics)
Sun, Han (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.4, 2017 , pp. 2109-2123 More about this Journal
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.
Keywords
Two-dimensional bar code; super-resolution; sparse representation; texture feature;
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