针对现有的红外与可见光图像融合算法处理后的图像存在对比度低与细节模糊等问题，提出了一种结合区域与边缘特征的红外与可见光图像融合算法；该算法根据红外与可见光图像边缘与信号域中信号的分布特点，分别采用边缘检测与区域相似度来度量图像信号的不同；其中边缘轮廓采用能量的加权融合算法，而区域信息则是采用区域相关信号强度识别技术。实验结果表明，该算法的融合效果与其他融合算法相比，该算法能够获得更为清晰的背景与目标图像，在主观视觉与客观数据方面均有较大的改善。

Images processed using the existing infrared and visible image fusion algorithm suffer from low contrast and detail blur. Hence, an infrared and visible image fusion algorithm based on the central and fringe image characteristics is proposed. It individually utilizes the edge detection or area similarity to measure the difference between images according to the distribution characteristics of the edge signal and domain signal by infrared and visible images. The edge contour signal is to be further integrated using the weighted energy fusion algorithm and the area signal is to be further integrated using the correlation signal of the strong areas .Our experimental results demonstrate that such an algorithm yields a clearer picture background and objectives and greatly improves the subjective and objective visual data.