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Proceedings Paper

Polarization image fusion algorithm based on improved PCNN
Author(s): Siyuan Zhang; Yan Yuan; Lijuan Su; Liang Hu; Hui Liu
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Paper Abstract

The polarization detection technique provides polarization information of objects which conventional detection techniques are unable to obtain. In order to fully utilize of obtained polarization information, various polarization imagery fusion algorithms have been developed. In this research, we proposed a polarization image fusion algorithm based on the improved pulse coupled neural network (PCNN). The improved PCNN algorithm uses polarization parameter images to generate the fused polarization image with object details for polarization information analysis and uses the matching degree M as the fusion rule. The improved PCNN fused image is compared with fused images based on Laplacian pyramid (LP) algorithm, Wavelet algorithm and PCNN algorithm. Several performance indicators are introduced to evaluate the fused images. The comparison showed the presented algorithm yields image with much higher quality and preserves more detail information of the objects.

Paper Details

Date Published: 19 December 2013
PDF: 8 pages
Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90450B (19 December 2013); doi: 10.1117/12.2037173
Show Author Affiliations
Siyuan Zhang, Beihang Univ. (China)
Yan Yuan, Beihang Univ. (China)
Lijuan Su, Beihang Univ. (China)
Liang Hu, Beihang Univ. (China)
Hui Liu, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 9045:
2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Xinggang Lin; Jesse Zheng, Editor(s)

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