Image fusion algorithm based on PCA & self-adaptive region variance
基于PCA和自适应区域方差的图像融合方法

This paper proposed a novel image fusion algorithm based on PCA and self-adaptive local region variance according to the different characteristic of the coefficients of low frequency and high frequency after lifting wavelet transform on the original image, that was, used a weighted method depending on principal component analysis in the low frequency image, and selected an algorithm called self-adaptive local region variance as the guide to the high frequency images. At last, obtained the fused image by applying inverse lifting wavelet transform. Compared with other traditional methods based on wavelet transform, the experimental results show that this algorithm not only increases entropy and average gradient effectively, but also improves the correlation coefficient, reduces the degree of distortion, holds detail information of original images and provides good visual effects.