一种基于NSST变换的红外偏振图像融合算法研究

红外成像技术可以全天候进行工作,受一些恶劣天 气因素的干扰较少,因为其依靠目标的温度差和热辐射率来成像,缺点也很明显,成像结果 细节模糊,目标与背景的对比度低,边缘平滑,视觉效果一般。而偏振图像由于其只保留某 些特定方向入射光的特殊成像机制,从而可以获取目标与背景粗糙度、含水量、物质理化以 及纹理特征等多维细节信息,但也正因为滤除了部分方向的光线,导致偏振图像整体亮度偏 低。针对单偏振参量信息弱、对比度小而且红外强度图像细节模糊的问题,提出一种基于非 下采样剪切波变换(NSST)的红外偏振图像融合算法,可以有效提高复杂背景中目标的辨识 度。首先将偏振度图像与偏振角图像采用改进的区域方差方法进行融合得到初始图像。然后 对初始图像与红外强度图像进行NSST分解处理,低频分量采用区域关联度与区域方差相结合 的融合规则,高频分量的融合采用区域关联度与区域特性能量相结合的规则。实验结果表明 ,本算法主观视觉效果良好,客观评价指标也优于其他算法。

Research on an infrared polarized image fusion algorithm based on NSST transform

Infrared imaging technology can work a round the clock,ignoring the interference of weather factors,because it relies o n the temperature difference and thermal emissivity of the target to image.It al so has obvious disadvantages,such as high brightness but fuzzy details,low contr ast between the target and the background,smooth edges and general visual effect .Polarized images can obtain multi-dimensional details such as target and backg round roughness,water content,physicochemical properties and texture features be cause they only retain the special imaging mechanism of incident light in a spec ific direction.However,polarized images are generally low in brightness due to t he filtering of light in most directions.Aiming at the problem about weak inform ation and low contrast of single polarization parameter image,an infrared polari zation image fusion algorithm based on non-subsampling shear wave transform (NS ST) is proposed to effectively improve target recognition in complex background. First of all,the image of polarization degree and polarization angle are fused u sing an improved regional variance method to obtain the initial fusion image.The n perform NSST decomposition processing on the initial image and the infrared in tensity image,the low frequency component adopts the fusion rule that combines t he regional correlation degree and regional variance,while the fusion rules of t he high frequency component resort the regional characteristic energy.Experiment al results show that the subjective visual effect of this algorithm is excellent ,and the objective evaluation index is also better than other algorithms.