基于结构与分解的红外光强与偏振图像融合

在一些特定环境下，红外传感器无法探测到目标时，需要将偏振技术与红外技术相融合。为了获得更清楚的融合图像，采用一种基于多尺度结构分解的图像融合方法实现红外光强与偏振图像融合。该算法提出将红外图像与偏振图分解成3个独立部分：平均强度、信号强度和信号结构。其中平均强度部分，采用一种反正切的权重函数进行融合，信号强度采用最大值的融合原则，而信号结构采用一种基于信号强度幂函数的加权平均方进行融合，最后重构得到融合图像。为了更快进行融合、降低计算的复杂度，将分解过程通过均值滤波代替，再通过上采样与下采样得到最终的融合图像。为了得到更好的融合图像，通过不同融合参数实验对比，选择较优的融合参数。最后实验表明使用所提出的反正切权重函数与融合参数设置，在与传统的多尺度算法的比较中，4项评价指标取得优势，且主观上保留更多的纹理细节、提升对比度以及抑制伪影。

Fusion of Infrared Intensity and Polarized Images Based on Structure and Decomposition

In specific environments, when an infrared sensor cannot detect a target, it is necessary to integrate polarization and infrared technologies. To obtain a clearer fused image, this study adopted a method based on a multiscale structure and feature image fusion to realize infrared and polarization image fusion. The algorithm decomposed the infrared image and polarization map into three independent parts: average intensity, signal intensity, and signal structure. An arctangent weight function was proposed for fusion in the average intensity part, the signal intensity adopted the maximum fusion principle, and the signal structure adopted a weighted average square based on the power function of the signal intensity for fusion, and finally, the fused image was reconstructed. To fuse faster and reduce computational complexity, the decomposition process was replaced with mean filtering, and the final fused image was obtained by upsampling and downsampling. To obtain a better fusion image, better fusion parameters were selected through an experimental comparison of different fusion parameters. Experiments showed that by using the proposed arctangent weight function and fusion parameter setting, the four evaluation indexes had advantages over the traditional multiscale algorithm and subjectively retained more texture details, improved contrast, and suppressed artifacts.