Li Xin'e, Ren Jianyue, Lv Zengming, Sha Wei, Zhang Liguo, He Bin. Fusion method of multispectral and panchromatic images based on improved PCNN and region energy in NSCT domain[J]. Infrared and Laser Engineering, 2013, 42(11): 3096-3102.
| Citation:
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Li Xin'e, Ren Jianyue, Lv Zengming, Sha Wei, Zhang Liguo, He Bin. Fusion method of multispectral and panchromatic images based on improved PCNN and region energy in NSCT domain[J]. Infrared and Laser Engineering, 2013, 42(11): 3096-3102.
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Fusion method of multispectral and panchromatic images based on improved PCNN and region energy in NSCT domain
- 1.
Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;
- 2.
University of Chinese Academy of Sciences,Beijing 100049,China
- Received Date: 2013-03-11
- Rev Recd Date:
2013-04-13
- Publish Date:
2013-11-25
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Abstract
A fusion method of multispectral(MS) and panchromatic(PAN) images based on improved Pulse-Coupled Neural Network(PCNN) and region energy in Nonsubsampled Contourlet Transform(NSCT) domain was proposed. Firstly, the two original images were decomposed into a low frequency subband and more bandpass directional subbands by NSCT. Then, for the low frequency subband coefficients, an adaptive regional energy weighting image fusion algorithm was presented; while for the bandpass directional subband coefficients, based on improved PCNN, the bandpass directional subband coefficients was used as the linking strength. After processing PCNN with the linking strength, new fire mapping images were obtained. The fire mapping image region energy was calculated, and the fusion coefficients were decided by the compare-selection operator with the fire mapping image region energy. Finally, the fusion images were reconstructed by NSCT inverse transform. The experimental results show that, when the numbers of iterations are 100 times, respectively as comparing with that of improved wavelet method, Contourlet method and NSCT method: the standard deviation increases by 9.48%, 9.73% and 3.84%; the entropy by 0.95%, 0.94% and 3.34%; the correlation coefficient by 21.56%, 11.27% and 7.89%, and the deviation index reduces by 29.66%, 9.45% and 7.42%.
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