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Mathematical Problems in Engineering
Volume 2015, Article ID 536215, 9 pages
http://dx.doi.org/10.1155/2015/536215
Research Article

PCNN-Based Image Fusion in Compressed Domain

Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received 8 October 2014; Revised 6 January 2015; Accepted 7 January 2015

Academic Editor: Yi-Kuei Lin

Copyright © 2015 Yang Chen and Zheng Qin. 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. S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image and Vision Computing, vol. 26, no. 7, pp. 971–979, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Huang and Z. Jing, “Multi-focus image fusion using pulse coupled neural network,” Pattern Recognition Letters, vol. 28, no. 9, pp. 1123–1132, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. X.-H. Yang, H.-Y. Jin, and L.-C. Jiao, “Adaptive image fusion algorithm for infrared and visible light images based on DT-CWT,” Journal of Infrared and Millimeter Waves, vol. 26, no. 6, pp. 419–424, 2007. View at Google Scholar · View at Scopus
  4. Y. Niu, S. Xu, L. Wu, and W. Hu, “Airborne infrared and visible image fusion for target perception based on target region segmentation and discrete wavelet transform,” Mathematical Problems in Engineering, vol. 2012, Article ID 275138, 10 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. C. S. Pattichis, M. S. Pattichis, and E. Micheli-Tzanakou, “Medical imaging fusion applications: an overview,” in Proceedings of the 35th Asilomar Conference on Signals, Systems and Computers (ACSSC ’01), pp. 1263–1267, November 2001. View at Scopus
  6. R. Shen, I. Cheng, and A. Basu, “Cross-scale coefficient selection for volumetric medical image fusion,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 4, pp. 1069–1079, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Yang and S. T. Li, “Pixel-level image fusion with simultaneous orthogonal matching pursuit,” Information Fusion, vol. 13, no. 1, pp. 10–19, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Yang, C. Z. Han, X. Kang, and D. Q. Han, “An overview on pixel-level image fusion in remote sensing,” in Proceedings of the IEEE International Conference on Automation and Logistics (ICAL '07), pp. 2339–2344, Jinan, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Ross and R. Govindarajan, “Feature level fusion using hand and face biometrics,” in Biometric Technology for Human Identification II, Proceedings of SPIE, pp. 196–204, 2005.
  10. J. Byeungwoo and D. Landgrebe, “Decision fusion approach for multitemporal classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 3, pp. 1227–1233, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. W. C. Wang and F. L. Chang, “A multi-focus image fusion method based on Laplacian pyramid,” Journal of Computers, vol. 6, no. 12, pp. 2559–2566, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. V. S. Petrović and C. S. Xydeas, “Gradient-based multiresolution image fusion,” IEEE Transactions on Image Processing, vol. 13, no. 2, pp. 228–237, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Amolins, Y. Zhang, and P. Dare, “Wavelet based image fusion techniques—an introduction, review and comparison,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 62, no. 4, pp. 249–263, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Pajares and J. M. de la Cruz, “A wavelet-based image fusion tutorial,” Pattern Recognition, vol. 37, no. 9, pp. 1855–1872, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Shi, C. Zhu, Y. Tian, and J. Nichol, “Wavelet-based image fusion and quality assessment,” International Journal of Applied Earth Observation and Geoinformation, vol. 6, no. 3-4, pp. 241–251, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Yang, M. Wang, L. Jiao, R. Wu, and Z. Wang, “Image fusion based on a new contourlet packet,” Information Fusion, vol. 11, no. 2, pp. 78–84, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. Q. Zhang and B.-L. Guo, “Multifocus image fusion using the nonsubsampled contourlet transform,” Signal Processing, vol. 89, no. 7, pp. 1334–1346, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Li and B. Yang, “Hybrid multiresolution method for multisensor multimodal image fusion,” IEEE Sensors Journal, vol. 10, no. 9, pp. 1519–1526, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. 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
  20. E. J. Candès 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
  21. J. Han, O. Loffeld, K. Hartmann, and R. Wang, “Multi image fusion based on compressive sensing,” in Proceedings of IEEE International Conference on Image Processing (ICIP '10), pp. 1463–1469, 2010.
  22. T. Wan, N. Canagarajah, and A. Achim, “Compressive image fusion,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '08), pp. 1308–1311, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. L. Gan, T. T. Do, and T. D. Tran, “Fast compressive imaging using scrambled block hadamard ensemble,” in Proceedings of the 16th European Signal Processing Conference (EUSIPCO '08), August 2008. View at Scopus
  25. H. Li, B. S. Manjunath, and S. K. Mitra, “Multisensor image fusion using the wavelet transform,” Graphical Models and Image Processing, vol. 57, no. 3, pp. 235–245, 1995. View at Publisher · View at Google Scholar · View at Scopus
  26. I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer, 1986. View at Publisher · View at Google Scholar · View at MathSciNet
  27. U. Patil and U. Mudengudi, “Image fusion using hierarchical PCA,” in Proceedings of the International Conference on Image Information Processing (ICIIP '11), pp. 1–6, Himachal Pradesh, India, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. X. Y. Luo, J. Zhang, J. Y. Yang, and Q. H. Dai, “Image fusion in compressed sensing,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '09), pp. 2205–2208, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Qu, D. Zhang, and P. Yan, “Information measure for performance of image fusion,” Electronics Letters, vol. 38, no. 7, pp. 313–315, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE Transactions on Information Theory, vol. 52, pp. 5406–5425, 2006. View at Google Scholar
  31. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Review, vol. 43, no. 1, pp. 129–159, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences, vol. 1, no. 3, pp. 248–272, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  33. J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655–4666, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. E. J. Candès 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
  35. E. Candès and J. Romberg, “l1-magic: Recovery of Sparse Signals via Convex Programming,” 2005.
  36. R. Eckhorn, H. J. Reitboeck, M. Arndt, and P. W. Dicke, “Feature linking via synchronization among distributed assemblies: simulation of results from cat cortex,” Neural Computation, vol. 2, pp. 293–307, 1990. View at Google Scholar
  37. C. S. Xydeas and V. Petrović, “Objective image fusion performance measure,” Electronics Letters, vol. 36, no. 4, pp. 308–309, 2000. View at Publisher · View at Google Scholar · View at Scopus