针对可见光与红外图像的特点和难点，提出了可见光与红外图像配准与融合中的关键技术，即:

使用新型的基于一维最大类间方差和最大连通性测量的图像分割方法对源图像进行分割来更好地实行图像粗

配准 使用新型的特征点提取方法，特征点的匹配及误匹配的消除来更好地实行图像精配准 采用新型的基

于区域的树状小波活性测度计算来实现树状小波图像融合 利用自生成神经网络来实现模栩图像融合.

-For visible light and infrared images of the characteristics and difficulties, a visual and infrared image registration and integration of key technologies, namely: the use of new one-dimensional based on the largest variance between-class connectivity and the largest measuring method of image segmentation source image segmentation to better implementation of coarse image registration　the use of a new type of feature point extraction method, feature points of the match and the elimination of false matches to better the implementation of image fine registration　the use of new tree-based wavelet calculated to measure the achievement of the activity tree wavelet image fusion　the use of self-generating neural network to achieve image fusion Xu mode.