基于互信息和梯度的红外与可见光图像配准新方法

Novel infrared-visual image registration based on combined mutual and gradient information

红外与可见光图像配准是常见的多传感器图像配准,在军事、遥感等领域有着广泛的应用。提出了一种基于互信息和图像梯度的红外与可见光图像的自动配准方法:首先,获得图像的梯度信息,然后根据定义的扩展结构获得边缘区域图像,选择最大归一化互信息作为相似性测度,使用Powell算法获得最佳配准参数。实验结果证明,本文方法较传统的基于互信息和梯度的配准方法,提高了配准的速度和精度,可以作为一种有效的粗配准的方法。

Infrared-visual image registration is a common multi-sensor image processing task, which played an important role in the fields of remote sensing and military. In this paper, a novel and automatic method based on mutual information and gradient information is proposed for the infrared-visual image registration：Firstly, get the gradient information of the image,then the image edge can be obtained by using a structure tensor;the maximum normalized mutual information is used as similarity measure and also the Powell algorithm is used to search for the optimal registration parameters. Experimental results show that compared to the traditional method based on gradient and mutual information, the method proposed in this paper has higher accuracy and needs less time. It can be used as an effective method of coarse registration.