为了提升红外和可见光图像融合效果，提出了一种新颖的融合方法。对可见光图像利用支持向量机和腐蚀膨胀算法在图像分块的基础上进行分割获取含有重要场景信息的特征子图像和灰度场景子图像；对红外图像进行热目标边缘提取并增强，结合前述特征子图像和最大类间方差法获取边缘增强的目标子图像，特征子图像和灰度场景子图像。利用小波包分别对两特征子图像，两场景子图像进行融合。融合过程中，根据子图像特点择取不同融合准则，并对高频融合系数进行系数修正使其更为准确可靠。将红外热目标注入到前述融合结果中获取最终融合图像。实验结果表明，提出算法从主、客观评价上都要优于对比算法。

To improve the fusion effect of infrared and visible light images, a novel fusion method is proposed. The visible light image is segmented into feature subimages with important scene information and grayscale scene subimages using a support vector machine and corrosion expansion algorithm based on the image block. The hot target edge of the infrared image is extracted and enhanced. Target, feature, and gray background subimages are obtained by combining the information of the former feature subimage with the maximum interclass variance method. Two feature subimages(grayscale scene and gray background) are fused by wavelet packet transform. During fusion, different fusion rules are implemented according to the characteristics of the subimages. Additionally, high frequency fusion coefficients are modified to render them more accurate and reliable. The infrared thermal target is injected into the previous fusion result to obtain the final fusion image. Experimental results show that the proposed algorithm is superior to other algorithms both in subjective and objective evaluations.