%0 Journal Article
%T Modified PCNN Based Multisensor Image Fusion Scheme<br>基于修正PCNN的多传感器图像融合方法
%A LI Min
%A CAI Cheng
%A TAN Zheng
%A <br>李敏
%A 蔡骋
%A 谈正
%J 中国图象图形学报
%D 2008
%I 
%X Being an efficient method of information fusion,multisensor image fusion has been used in many fields such as machine vision,medical diagnosis,military applications and remote sensing.In this paper,PCNN is introduced in this research field for its interesting properties in image processing,including segmentation,target recognition et al.,and a multisensor image fusion scheme based on modified PCNN is proposed.The basic idea of the scheme is to segment all different input images by PCNN and to use this segmentation to guide the fusion process.At the same time,a new region feature,which emphasized the salience of target regions and its neighbors is proposed.Focusing on the famous difficult problem of PCNN,how to determine PCNN parameters adaptively,an adaptive PCNN parameters determination algorithm is also presented in this paper.Experimental results demonstrate that the proposed fusion scheme outperforms the multiscale decomposition based fusion approaches,both in visual effect and objective evaluation criteria.It avoids some of the well-known problems in pixel-level fusion such as blurring effects and high sensitivity to noise,particularly when there is mis-registration of the source images.The research fruits have certain value on the theory research and practical application of PCNN.
%K multisensor image fusion
%K pulse-coupled neural network(PCNN)
%K parameter determination
%K objective evaluation criteria<br>多传感器图像融合
%K 脉冲耦合神经网络
%K 参数设定
%K 客观评价准则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=74EEFF61CA5379D3CEE71FF625D64035&yid=67289AFF6305E306&vid=FC0714F8D2EB605D&iid=0B39A22176CE99FB&sid=03E56C113B4E5A88&eid=211C7E02AC474301&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=1&reference_num=10