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Journal of Sensors
Volume 2016, Article ID 8359602, 12 pages
http://dx.doi.org/10.1155/2016/8359602
Research Article

Multifocus Color Image Fusion Based on NSST and PCNN

Information College, Yunnan University, Kunming 650091, China

Received 7 August 2015; Revised 29 October 2015; Accepted 5 November 2015

Academic Editor: Claudio Lugni

Copyright © 2016 Xin Jin 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. V. Petrović and V. Dimitrijević, “Focused pooling for image fusion evaluation,” Information Fusion, vol. 22, pp. 119–126, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Xia and Sh. R. Qu, “Color image fusion framework based on improved (2D)2PCA,” Acta Optica Sinica, vol. 34, no. 10, Article ID 1010001, 2014 (Chinese). View at Publisher · View at Google Scholar · View at Scopus
  3. R. H. Miao, J. L. Tang, and X. Q. Chen, “Classification of farmland images based on color features,” The Journal of Visual Communication and Image Representation, vol. 29, pp. 138–146, 2015. View at Google Scholar
  4. S. Daneshvar and H. Ghassemian, “MRI and PET image fusion by combining IHS and retina-inspired models,” Information Fusion, vol. 11, no. 2, pp. 114–123, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. M. González-Audícana, J. L. Saleta, R. G. Catalán, and R. García, “Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 6, pp. 1291–1299, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Xin and L. Deng, “An improved remote sensing image fusion method based on wavelet transform,” Laser & Optoelectronics Progress, vol. 50, no. 2, 2013. View at Google Scholar
  7. E. J. Candes and D. L. Donoho, “Curvelets: a surprisingly effective nonadaptive representation of objects with edges,” in Curves and Surfaces Fitting, pp. 105–120, Vanderbilt University Press, Nashville, Tenn, USA, 2000. View at Google Scholar
  8. M. N. Do and M. Vetterli, Contourlets Beyond Wavelets, edited by: G. V. Welland, Academic Press, 2003.
  9. D. Labate, W.-Q. Lim, G. Kutyniok, and G. Weiss, “Sparse multidimensional representation using shearlets,” in Wavelets XI, vol. 5914 of Proceedings of SPIE, pp. 254–262, San Diego, Calif, USA, September 2005. View at Publisher · View at Google Scholar
  10. Z.-Y. Fan, Q.-S. Sun, Z.-X. Ji, and K. Hu, “An image filter based on multiobjective genetic algorithm and shearlet transformation,” Mathematical Problems in Engineering, vol. 2013, Article ID 463760, 7 pages, 2013. View at Publisher · View at Google Scholar
  11. G. Easley, D. Labate, and W.-Q. Lim, “Sparse directional image representations using the discrete shearlet transform,” Applied and Computational Harmonic Analysis, vol. 25, no. 1, pp. 25–46, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. W. Kong, L. Zhang, and Y. Lei, “Novel fusion method for visible light and infrared images based on NSST-SF-PCNN,” Infrared Physics and Technology, vol. 65, pp. 103–112, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. R. C. Nie, D. M. Zhou, M. He, X. Jin, and J. Yu, “Facial feature extraction using frequency map series in PCNN,” Journal of Sensors, In press.
  14. M. M. Subashini and S. K. Sahoo, “Pulse coupled neural networks and its applications,” Expert Systems with Applications, vol. 41, no. 8, pp. 3965–3974, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. C. H. Zhao, G. F. Shao, L. J. Ma, and X. Zhang, “Image fusion algorithm based on redundant-lifting NSWMDA and adaptive PCNN,” Optik, vol. 125, no. 20, pp. 6247–6255, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. W. W. Kong, L. J. Zhang, and Y. Lei, “Novel fusion method for visible light and infrared images based on NSST-SF-PCNN,” Infrared Physics & Technology, vol. 65, pp. 103–112, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Yuan, B. X. Yang, Y. D. Ma, J. Zhang, R. Zhang, and C. Zhang, “Compressed sensing MRI reconstruction from highly undersampled k-space data using nonsubsampled shearlet transform sparsity prior,” Mathematical Problems in Engineering, vol. 2015, Article ID 615439, 18 pages, 2015. View at Publisher · View at Google Scholar
  18. J.-F. Pekel, C. Vancutsem, L. Bastin et al., “A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data,” Remote Sensing of Environment, vol. 140, pp. 704–716, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. L. M. Dong, Q. X. Yang, H. Y. Wu, H. Xiao, and M. Xu, “High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform,” Neurocomputing, vol. 159, pp. 268–274, 2015. View at Publisher · View at Google Scholar
  20. M. J. Kim, D. K. Han, and H. Ko, “Joint patch clustering-based dictionary learning for multimodal image fusion,” Information Fusion, vol. 27, pp. 198–214, 2016. View at Publisher · View at Google Scholar
  21. P. Zhang, C. Fei, Z. M. Peng et al., “Multifocus image fusion using biogeography-based optimization,” Mathematical Problems in Engineering, vol. 2015, Article ID 340675, 14 pages, 2015. View at Publisher · View at Google Scholar
  22. S. T. Li, J. T. Kwok, and Y. N. Wang, “Combination of images with diverse focuses using the spatial frequency,” Information Fusion, vol. 2, no. 3, pp. 169–176, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Cheng, H. J. Liu, T. Liu, F. Wang, and H. Li, “Remote sensing image fusion via wavelet transform and sparse representation,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 104, pp. 158–173, 2015. View at Publisher · View at Google Scholar