本文阐述了基于主元分析的拉普拉斯金字塔图像融合的原理和方法:首先对原图像分别进行拉普拉斯

金字塔分解,然后分别对高频部分采用主元分析（PCA）法融合,对低频部分采用平均梯度法进行融合,最后对

拉普拉斯金字塔做反变换得到最终的融合图像。通过对可见光与红外图像的融合，以及对不同焦距图像融合

的结果分析，该算法比单纯的PCA和拉普拉斯图像融合能得到具有更多有用信息的高对比度的融合图像-In this paper, principal component analysis based on the Laplacian pyramid image fusion of theory and method: First, the original image, respectively Laplacian pyramid decomposition, and then were used for high-frequency part of the principal component analysis (PCA) fusion method for The average gradient method using low-frequency part of the fusion, the last of the Laplacian pyramid to do the inverse transform to get the final fused image. Through the integration of visual and infrared images, as well as the results of image fusion of different focal length of the algorithm than the simple PCA and Laplacian image fusion can get more useful information with the integration of high-contrast images