面向双模态红外图像融合算法选取的联合可能性落影构造

针对现实场景中双模态红外图像融合对异类差异特征协同优化融合的需求，且现有差异特征属性无法根据差异特征多个属性的变化针对性地调整融合算法进行有效驱动，导致融合效果差的问题，提出了面向双模态红外图像融合算法选取的联合可能性落影构造方法。首先计算双模态红外图像多融合算法下不同差异特征的融合有效度、统计差异特征分布特性；再构造差异特征融合有效度的可能性分布，通过最小二乘估计法拟合可能性分布函数；然后通过择优比较法对不同差异特征融合有效度的可能性分布进行对比分析，确定差异特征可能性分布函数投影权重，构造联合可能性落影函数；最后分析联合可能性落影函数截集水平，结合差异特征分布特性构建融合性能指标动态选取最优融合算法。实验结果表明，本文方法所选出的最优融合算法在主客观综合分析上优于其他算法，验证了本文将联合可能性落影运用于双模态红外图像最优融合算法选取中有效性和合理性。

Joint Possibility Drop Shadow Construction for Selection of Bimodal Infrared Image Fusion Algorithm

A joint likelihood drop shadow construction method for the selection of a bimodal infrared image fusion algorithm is proposed. It aims at the demand for the cooperative and optimal fusion of dissimilar disparity features in real scenes of bimodal infrared image fusion and the limitation that the existing disparity feature attributes cannot be effectively driven by the targeted adjustment of the fusion algorithm according to the changes in multiple attributes of the disparity features, resulting in a poor fusion effect. First, we calculate the fusion effectiveness of different disparity features under the multimodal infrared image fusion algorithm and statistical disparity feature distribution characteristics. We then construct the likelihood distribution of the disparity feature fusion effectiveness and fit the likelihood distribution function by the least squares estimation method. Subsequently, we compare and analyze the likelihood distribution of different disparity feature fusion effectiveness by the merit comparison method and determine the projection weights of the disparity feature likelihood distribution function. Finally, we analyze the intercept level of the joint possibility drop shadow function and construct the optimal fusion algorithm by combining the characteristics of the distribution of different features to dynamically select the fusion performance index. The experimental results show that the optimal fusion algorithm selected in this study outperforms other algorithms in terms of subjective and objective analyses, which verifies the effectiveness and rationality of applying the joint likelihood drop shadow to the selection of an optimal fusion algorithm for bimodal infrared images.