随着融合技术的发展、小波理论的成熟，小波变换以其良好的时频特性在图像融合领域脱颖而出。本文在小波变换理论的基础上，提出了一种结合小波分解的改进型PCNN图像融合新方法。首先对两幅已经配准的原始图像进行小波多尺度分解；然后基于改进后的脉冲耦合神经网络模型提出一种新的融合规则，文中重点针对小波分解后高频域和低频域的特点，分别在各频域采用不同的融合方法，最后通过逆小波变换重构图像得到融合后的图像。仿真结果和评价指标结果表明，此方法更好地保留了原图像中的有用信息，提高了融合图像的质量。

With the development of fusion technology and the growth of the wavelet theory,wavelet transform uses its excellent time and frequency localization,excels in the area of fusion.On the basis of the wavelet theory,we propose a new algorithm which combines wavelet transform with the pulse coupled neural networks.Firstly,we perform a wavelet multiscale decomposition of two original images which have been registered.Secondly, we propose a novel fusion rule based on the improved PCNN.The key point in this paper is that in allusion to the feature of the high frequency domain and the low frequency one,using various methods to each frequency domain,respectively.Finally,it can obtain a fused image by taking inverse wavelet transform to reconstruct images.The results of simulation and quantifying evaluation show that this algorirhm can preserve more useful information from the original images effectively,and enhance the quality of the fused image.