@inproceedings{bce87bfead19446683f53a4614c2c94f,
title = "Polarization image fusion algorithm based on improved PCNN",
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.",
keywords = "Image fusion, Matching degree, PCNN, Polarization",
author = "Siyuan Zhang and Yan Yuan and Lijuan Su and Liang Hu and Hui Liu",
year = "2013",
doi = "10.1117/12.2037173",
language = "英语",
isbn = "9780819499639",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "2013 International Conference on Optical Instruments and Technology",
note = "2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, OIT 2013 ; Conference date: 17-11-2013 Through 19-11-2013",
}