针对现有融合算法在处理红外偏振和光强两种模态图像时无法随着差异特征的不同而变化的问题, 提出了一种双模态红外图像的集值映射融合方法。以多组红外偏振和光强图像为训练样本, 根据底层特征的融合算法的类型分别建立差异特征类集和融合算法类集; 构造各个差异特征对应各融合算法的融合有效度分布; 通过多组图像融合有效度分布的均值聚合, 建立全局图像的差异特征与融合算法的集值映射关系; 提取被融合图像的差异特征, 根据集值映射选择相应算法, 通过各算法的组合得到融合结果。实验表明, 该融合方法可以优化选择融合算法, 将互补性强的差异特征有效融合。

To solve the problem that fusion algorithm is not optimal selection along with the change of difference characteristics when dealing with infrared polarization and infrared intensity of dual-mode image, a method of set-valued mapping fusion is presented. Multi-group of polarization and intensity of infrared images as training samples, different classification of low-level features and fusion algorithms are used to construct difference characteristics class set and fusion algorithm class set. Fusion effective measure distribution of each characteristic corresponding to fusion algorithms is constructed. Mean aggregating for multi-group images fusion effective measure distributions in order to establish the set-valued mapping relationship between difference characteristics and fusion algorithms of the global image. Difference characteristics of test images are extracted. Pick out corresponding algorithms according to the set-valued mapping. Then, get the fusion result by the combination of the algorithms. Experimental results show that the fusion method can select the optimal fusion algorithm, and have the highly complementary characteristics fused effectively.