塔型分解多源图像融合方法

A Multi-source Image Fusion Method of Tower Type Decomposition

针对红外与微光等不同设备在特定环境下各自的成像效果存在的不足,研究了红外与微光多源图像融合技术。对多源图像常用的融合方法尤其是基于金字塔分解的融合方法进行了研究,分析了图像融合中的塔形分解优点,提出了利用塔型分解图像融合的规则与流程,并分别对基于单个像素和基于像素区域的融合方式进行了详细的描述。通过实验研究了不同分解类型、不同分解层数目及不同融合方式对红外与微光图像融合效果的影响。实验结果表明,基于塔形分解的红外与微光图像融合方法在提高源图像可视度、信息熵、标准差以及平均梯度等图像指标方面可以明显改善图像的融合效果,在保留图像背景信息的同时,能够最大限度地反映和突出图像中的目标信息,有助于后续的目标检测与识别。

Aiming at the deficiency of the infrared and low light level imaging results under certain circumstances,we research the infrared and low light level image fusion technology.We study the common fusion methods of multi-source images,especially those based on pyramid decomposition,analyze the advantages of tower decomposition in image fusion,propose the rules and processes of image fusion based on tower decomposition,and describe the fusion methods based on single pixel and pixel area in detail.The effects of different decomposition types,different decomposition layers and different fusion modes on the fusion effect of infrared and low light level images are studied through experiments.It shows that the infrared and low light level image fusion method based on pyramid decomposition can obviously improve image fusion effect in enhancement of image indicators like visibility,information entropy,standard deviation,and average gradient,and reflect and highlight target information of image extremely while retaining image background information at the same time,which is helpful for the subsequent target detection and recognition.