针对现有的红外光强与偏振图像融合中融合有效度度量稳定性低且不符合人类视觉系统的问题，通过比较常见的基于距离度量的三种融合有效度函数化度量方法，分析比较了多种融合算法对差异特征融合有效度分布的稳定性；通过统计十组源图像的差异特征幅值区间中出现最优融合算法的频次，从而得出每种差异特征的最优融合算法，进而得到了余弦相似性为一种稳定性高且与人类主观观察结果更符合的融合有效度的度量方式。实验结果表明，在多种融合算法的融合有效度度量的融合效果中，余弦相似性具有高稳定性且与人类视觉分析有良好的匹配性。

To solve the problem of low stability of fusion validity measurement in existing fusion of infrared intensity and polarization images, the stability of various fusion algorithms for the distribution of fusion validity of different features was analyzed and compared by using three common fusion validity function measurement methods based on measurement distance. By calculating the frequency of the optimal fusion algorithm in difference feature amplitude interval of ten groups of images, the optimal fusion algorithms for each difference feature were obtained, and cosine similarity was obtained as a measure of fusion efficiency with high stability and more consistent with subjective observation results of the humans. The experimental results show that the cosine similarity has high stability and good matching with human vision analysis in the fusion effectiveness measurement of various fusion algorithms.