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Journal of Sensors
Volume 2016, Article ID 6370845, 12 pages
http://dx.doi.org/10.1155/2016/6370845
Research Article

An Efficient Image Enlargement Method for Image Sensors of Mobile in Embedded Systems

1College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610064, China
2College of Electrical and Engineering Information, Sichuan University, Chengdu, Sichuan 610064, China
3School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China

Received 20 November 2015; Revised 5 March 2016; Accepted 21 March 2016

Academic Editor: Marco Anisetti

Copyright © 2016 Hua Hua et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Kimmel, “Demosaicing: image reconstruction from color CCD samples,” IEEE Transactions on Image Processing, vol. 8, no. 9, pp. 1221–1228, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. X. Li, “Demosaicing by successive approximation,” IEEE Transactions on Image Processing, vol. 14, no. 3, pp. 370–379, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Shen, L. Zhang, B. Huang, and P. Li, “A MAP approach to joint motion estimation, segmentation, and super resolution,” IEEE Transactions on Image Processing, vol. 16, no. 2, pp. 479–490, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. L. Zhang, H. Zhang, H. Shen, and P. Li, “A super-resolution reconstruction algorithm for surveillance images,” Signal Processing, vol. 90, no. 3, pp. 848–859, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Transactions on Image Processing, vol. 13, no. 10, pp. 1327–1344, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. 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. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Chang, D.-Y. Yeung, and Y. Xiong, “Super-resolution through neighbor embedding,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), vol. 1, pp. 275–282, July 2004. View at Scopus
  8. K. Zhang, X. Gao, X. Li, and D. Tao, “Partially supervised neighbor embedding for example-based image super-resolution,” IEEE Journal on Selected Topics in Signal Processing, vol. 5, no. 2, pp. 230–239, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Wang and X. Tang, “Hallucinating face by eigen transformation,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 35, no. 3, pp. 425–434, 2005. View at Publisher · View at Google Scholar
  10. W. Wu, Z. Liu, and X. He, “Learning-based super resolution using kernel partial least squares,” Image and Vision Computing, vol. 29, no. 6, pp. 394–406, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Monaci and P. Vandergheynst, “Learning structured dictionaries for image representation,” in Proceedings of the International Conference on Image Processing (ICIP '04), pp. 2351–2354, IEEE, October 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” in Curves and Surfaces, J.-D. Boissonnat, P. Chenin, A. Cohen et al., Eds., vol. 6920 of Lecture Notes in Computer Science, pp. 711–730, Springer, New York, NY, USA, 2012. View at Publisher · View at Google Scholar
  14. 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. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. X. Yang, K. Liu, Z. Gan, and B. Yan, “Multiscale and multitopic sparse representation for multisensor infrared image superresolution,” Journal of Sensors, vol. 2016, Article ID 7036349, 14 pages, 2016. View at Publisher · View at Google Scholar
  16. 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. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. Z. Zhan, J. Cai, D. Guo, Y. Liu, Z. Chen, and X. Qu, “Fast multi-class dictionaries learning with geometrical directions in MRI reconstruction,” IEEE Transactions on Biomedical Engineering, 2015. View at Publisher · View at Google Scholar
  18. R. Timofte, V. De, and L. V. Gool, “Anchored neighborhood regression for fast example-based super-resolution,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 1920–1927, Sydney, Australia, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. W. Wu, X. Yang, Y. Pang, J. Peng, and G. Jeon, “A multifocus image fusion method by using hidden Markov model,” Optics Communications, vol. 287, pp. 63–72, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Perd'och, O. Chum, and J. Matas, “Efficient representation of local geometry for large scale object retrieval,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '09), pp. 9–16, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar
  21. A. K. Jain, N. K. Ratha, and S. Lakshmanan, “Object detection using gabor filters,” Pattern Recognition, vol. 30, no. 2, pp. 295–309, 1997. View at Publisher · View at Google Scholar · View at Scopus