红外图像与可见光图像运用传统图像融合技术进行图像融合时,融合图像出现细节模糊、对比度降低等问题。针对此问题,提出了一种通过面积比改进脉冲耦合神经网络(PCNN)结合NSCT的图像融合方法。首先利用直方图双向均衡化对源图像预处理;其次经过NSCT分解图像得到低频子带和高频子带,高频部分采用改进的PCNN作为融合规则得到高频融合系数,低频部分采用加权平均作为融合规则得到低频融合系数;最后NSCT逆变换处理高低频融合系数得到融合图像。实验结果表明,融合算法在保留图像细节信息、增强图像轮廓信息方面优于传统图像融合算法,提高了图像对比度。
Abstract
When infrared images and visible light images use traditional image fusion technology for image fusion, there are the problems such as blurred details and decreased contrast in the fusion image.In response to such problems,this paper proposes an image fusion method of combining NSCT through the area-ratio of pulse coupling neural network (PCNN).First of all,the two way bidding of the histogram is pre-processing the source image.Secondly,through the NSCT decomposition image,the low-frequency belt and high frequency belt are obtained.The fusion rule obtains the low-frequency fusion coefficient.Finally,the NSCT inverter replace the processing of high and low frequency fusion coefficients to obtain the fusion image.The experiment results show that the fusion algorithm of this article is superior to traditional image fusion algorithms in retaining image information and enhancing image contour information,which improves image contrast.
关键词
NSCT /
PCNN /
面积比 /
直方图双向均衡化 /
加权平均
Key words
NSCT /
PCNN /
area ratio /
right-directional diagram two-way equilibrium /
weighted average
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