为了优化加权多分辨率图像融合的算法结构, 在Piella像素级多分辨率图像融合框架基础上, 提出一种只用匹配测度控制决策模块的加权多分辨率图像融合扩展模式, 改变了传统加权多分辨率图像融合模式必须由活性测度和匹配测度共同决定决策因子的格局。相对于传统模式, 提出的扩展模式去除了活性测度, 相应的算法结构更为简单。以相关信号强度比作为匹配测度, 给出了一种基于扩展模式的加权多分辨率图像融合算法。对红外和可见光图像的融合实验表明, 该算法融合性能优于传统加权多分辨率图像融合算法, 其边缘融合质量指标(EFQI)和加权融合质量指标(WFQI)分别提高了2.9%和1.8%; 而且计算复杂度更低, 计算决策因子所需的乘法和加法运算次数分别减少了66.7%和33.3%。

To optimize the architecture of weighted average multiresolution image fusion algorithm, an extended weighted average multiresolution image fusion model to control the decision map only with the match measure is presented based on the Piella pixel-level multiresolution image fusion framework. The extended model changes the fusion pattern that the decision factor must be decided by both activity measure and match measure in the conventional weighted average multiresolution image fusion model. Since the activity measure is removed in the extended model, the corresponding algorithm architecture is rather simpler. By taking the correlated signal intensity ratio as the match measure, a weighted average multiresolution image fusion method based on the extended model is proposed. The experiment on fusing infrared and visible images shows that the proposed method is able to produce better fusion results than the conventional ones and its Edge Fusion Quality Index(EFQI) and Weight Fusion Quality Index(WFQI)has increased by 2.9% and 1.8%, respectively.Moreover, it achieves much lower computational complexity for the decision factor with decreased multiplication and addition times of 66.7% and 33.3%, respectively.