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Mobile Information Systems
Volume 2017 (2017), Article ID 2314062, 12 pages
https://doi.org/10.1155/2017/2314062
Research Article

Statistical Prior Aided Separate Compressed Image Sensing for Green Internet of Multimedia Things

Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

Correspondence should be addressed to Jian Jiao

Received 16 December 2016; Accepted 22 February 2017; Published 16 March 2017

Academic Editor: Nan Zhao

Copyright © 2017 Shaohua Wu 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. N. Lu, N. Cheng, N. Zhang, X. Shen, and J. W. Mark, “Connected vehicles: solutions and challenges,” IEEE Internet of Things Journal, vol. 1, no. 4, pp. 289–299, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. N. Zhang, S. Zhang, S. Wu, J. Ren, J. W. Mark, and X. Shen, “Beyond coexistence: traffic steering in LTE networks with unlicensed bands,” IEEE Wireless Communications, vol. 23, no. 6, pp. 40–46, 2016. View at Publisher · View at Google Scholar
  3. N. Zhang, H. Liang, N. Cheng, Y. Tang, J. W. Mark, and X. S. Shen, “Dynamic spectrum access in multi-channel cognitive radio networks,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 11, pp. 2053–2064, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. N. Zhao, F. R. Yu, and H. Sun, “Adaptive energy-efficient power allocation in green interference-alignment-based wireless networks,” IEEE Transactions on Vehicular Technology, vol. 64, no. 9, pp. 4268–4281, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. S. A. Alvi, B. Afzal, G. A. Shah, L. Atzori, and W. Mahmood, “Internet of multimedia things: vision and challenges,” Ad Hoc Networks, vol. 33, pp. 87–111, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. E. J. Candes and M. B. Wakin, “An introduction to compressive sampling: a sensing/sampling paradigm that goes against the common knowledge in data acquisition,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21–30, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. E. Zimos, J. F. C. Mota, M. R. D. Rodrigues, and N. Deligiannis, “Internet-of-things data aggregation using compressed sensing with side information,” in Proceedings of the 23rd International Conference on Telecommunications (ICT '16), pp. 1–5, IEEE, Thessaloniki, Greece, May 2016. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Li, H. Huang, and S. Misra, “Compressed sensing via dictionary learning and approximate message passing for multimedia internet of things,” IEEE Internet of Things Journal, pp. 1–1, 2016. View at Publisher · View at Google Scholar
  10. Y. Rivenson and A. Stern, “Compressed imaging with a separable sensing operator,” IEEE Signal Processing Letters, vol. 16, no. 6, pp. 449–452, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Jokar, “Sparse recovery and kronecker products,” in Proceedings of the 44th Annual Conference on Information Sciences and Systems (CISS '10), pp. 1–4, March 2010.
  12. Y. Fang, J. Wu, and B. Huang, “2D sparse signal recovery via 2D Orthogonal matching pursuit,” Science China Information Sciences, vol. 55, no. 4, pp. 889–897, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. Y. Lin, S. Wu, J. Yu, and X. Lin, “Separate-combine recovery for compressed sensing of large images,” in Proceedings of the 1st IEEE International Conference on Communications (ICC '14), pp. 4601–4606, Sydney, Australia, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. 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. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. D. Needell and J. A. Tropp, “CoSaMP: iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis, vol. 26, no. 3, pp. 301–321, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. R. G. Baraniuk, “A signal-dependent time-frequency representation: fast algorithm for optimal kernel design,” IEEE Transactions on Signal Processing, vol. 42, no. 1, pp. 134–146, 1994. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080–2095, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. V. Cevher, “Learning with compressible priors,” in Advances in Neural Information Processing Systems, pp. 261–269, 2009. View at Google Scholar
  19. T. Tanaka and J. Raymond, “Optimal incorporation of sparsity information by weighted l1 optimization,” in Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT '10), Austin, Tex, USA, June 2010. View at Publisher · View at Google Scholar
  20. M. K. Mihçak, I. Kozintsev, K. Ramchandran, and P. Moulin, “Low-complexity image denoising based on statistical modeling of wavelet coefficients,” IEEE Signal Processing Letters, vol. 6, no. 12, pp. 300–303, 1999. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Kim, M. S. Nadar, and A. Bilgin, “Wavelet-based compressed sensing using a Gaussian scale mixture model,” IEEE Transactions on Image Processing, vol. 21, no. 6, pp. 3102–3108, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Transactions on Image Processing, vol. 12, no. 11, pp. 1338–1351, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus