KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2016.08.11
  • Accepted : 2017.02.07
  • Published : 2017.04.30
Download PDF
⟨ Previous Next ⟩

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

References

  1. H. Yang, A. C. Kot and X. Jiang, "Binarization of low-quality barcode images captured by mobile phones using local window of adaptive location and size," IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 418-425, 2012. https://doi.org/10.1109/TIP.2011.2155074
  2. C. Ren, X. He, Q. Teng, Y. Wu, and T. Q. Nguyen, "Single Image Super-Resolution Using Local Geometric Duality and Non-Local Similarity," IEEE Transactions on Image Processing, vol. 25, no. 5, pp. 2168-2183, 2016. https://doi.org/10.1109/TIP.2016.2542442
  3. K. Hung and W. Siu, "Robust soft-decision interpolation using weighted least squares," IEEE Transactions on Image Processing, vol. 21, no. 3, pp. 1061-1069, 2012. https://doi.org/10.1109/TIP.2011.2168416
  4. X. Zhang and X. Wu, "Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation," IEEE Transactions on Image Processing, vol. 17, no. 6, pp. 887-896, 2008. https://doi.org/10.1109/TIP.2008.924279
  5. L. Zhang and X. Wu, "An edge-guided image interpolation algorithm via directional filtering and data fusion," IEEE Transactions on Image Processing, vol. 15, no. 8, pp. 2226-2238, 2006. https://doi.org/10.1109/TIP.2006.877407
  6. K. Zhang, X. Gao, D. Tao, and X. Li, "Single image super-resolution with non-local means and steering kernel regression," IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4544-4556, 2012. https://doi.org/10.1109/TIP.2012.2208977
  7. Z. Lin and H. Shum, "Fundamental limits of reconstruction-based superresolution algorithms under local translation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 83-97, 2004. https://doi.org/10.1109/TPAMI.2004.1261081
  8. H. Chang, D. Yeung and Y. Xiong, "Super-resolution through neighbor embedding," in Proc. of Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, pp. I-I, 2004.
  9. W. T. Freeman, T. R. Jones and E. C. Pasztor, "Example-based super-resolution," IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56-65, 2002. https://doi.org/10.1109/38.988747
  10. J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution as sparse representation of raw image patches," in Proc. of Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1-8, 2008.
  11. K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. https://doi.org/10.1109/TPAMI.2010.25
  12. C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295-307, 2016. https://doi.org/10.1109/TPAMI.2015.2439281
  13. K. Zhang, X. Gao, X. Li, and D. Tao, "Partially supervised neighbor embedding for example-based image super-resolution," IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 2, pp. 230-239, 2011. https://doi.org/10.1109/JSTSP.2010.2048606
  14. B. A. Olshausen, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, no. 6583, pp. 607-609, 1996. https://doi.org/10.1038/381607a0
  15. J. Yang, J. Wright, T. S. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2861-2873, 2010. https://doi.org/10.1109/TIP.2010.2050625
  16. R. Zeyde, M. Elad and M. Protter, "On single image scale-up using sparse-representations," in Proc. of International Conference on Curves and Surfaces, pp. 711-730, 2010.
  17. W. Dong, L. Zhang and G. Shi, "Centralized sparse representation for image restoration," in Proc. of 2011 International Conference on Computer Vision, pp. 1259-1266, 2011.
  18. W. Dong, L. Zhang, G. Shi, and X. Li, "Nonlocally centralized sparse representation for image restoration," IEEE Transactions on Image Processing, vol. 22, no. 4, pp. 1620-1630, 2013. https://doi.org/10.1109/TIP.2012.2235847
  19. W. Dong, L. Zhang, G. Shi, and X. Wu, "Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization," IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 1838-1857, 2011. https://doi.org/10.1109/TIP.2011.2108306
  20. S. Yang, M. Wang, Y. Chen, and Y. Sun, "Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding," IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4016-4028, 2012. https://doi.org/10.1109/TIP.2012.2201491
  21. W. Liu and S. Li, "Sparse representation with morphologic regularizations for single image super-resolution," Signal Processing, vol. 98, no. 5, pp. 410-422, 2014. https://doi.org/10.1016/j.sigpro.2013.11.032
  22. T. Peleg and M. Elad, "A statistical prediction model based on sparse representations for single image super-resolution," IEEE Transactions on Image Processing, vol. 23, no. 6, pp. 2569-2582, 2014. https://doi.org/10.1109/TIP.2014.2305844
  23. J. Mairal, M. Elad and G. Sapiro, "Sparse representation for color image restoration," IEEE Transactions on Image Processing, vol. 17, no. 1, pp. 53-69, 2008. https://doi.org/10.1109/TIP.2007.911828
  24. D. L. Donoho and X. Huo, "Uncertainty principles and ideal atomic decomposition," IEEE Transactions on Information Theory, vol. 47, no. 7, pp. 2845-2862, 2001. https://doi.org/10.1109/18.959265
  25. E. J. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?" IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406-5425, 2006. https://doi.org/10.1109/TIT.2006.885507
  26. T. Ojala, M. Pietikainen and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  27. R. C. Gonzalez and R. E. Woods, Digital image processing, Prentice hall Upper Saddle River, NJ, 2008.
  28. G. Polatkan, M. Zhou, L. Carin, D. Blei, and I. Daubechies, "A bayesian nonparametric approach to image super-resolution," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 2, pp. 346-358, 2015. https://doi.org/10.1109/TPAMI.2014.2321404
  29. ISO/IEC18004, "Information technology - automatic identification and data capture techniques - qrcode bar code symbology specification," 2015.
  30. InliteResearch, "Clearimage barcode recognition sdk," https://www.inliteresearch.com/ barcode-recognition-sdk.php, 2016.