基于参数分步估计的红外与可见光图像自动配准算法

 提出了一种仿射模型参数分步估计的红外与可见光图像自动配准算法.首先,使用矩阵正交分解方法,将仿射变换的6个自由度分离为易于估计的切变、尺度比例、旋转、尺度缩放以及x和y方向上的平移量等参数;然后基于方向一致性约束和线段间的对齐度分别构建用于参数分步估计的目标函数,并使用SGA(Stud Genetic Algorithm)算法搜索使目标函数取得近似全局最优解的参数值;最后,基于Powell算法对参数估计值进行局部求精.实验结果表明,当两幅需要配准的图像中含有丰富的关联线段及多样的线段方向分布时,本文算法能够利用这些线段间的方向一致性约束和位置分布信息,有效地实现红外与可见光图像的自动配准,且算法具有较好的配准精度.

An Automatic Algorithm for Infrared and Visible Image Registration Based on Parameter Step Estimation

An automatic registration algorithm between infrared and visible images based on step estimation of affine model parameters is proposed.Firstly,the six degrees of freedom of the affine model are separated into some more easily estimated parameters using matrix orthogonal decomposition method,which are skew,scale ratio,rotation,scaling and translations in x and y directions.Secondly,two objective functions are constructed for step estimation of these parameters based on the orientation consensus constraint and alignment measure between segments,respectively,and parameter values which make objective function approximate the global optimum are obtained using SGA (Stud Genetic Algorithm) algorithm.Finally,the estimated values of the parameters are locally refined using Powell algorithm.The experimental results show that the proposed method can make full use of orientation consensus constraint and location distribution information of segments,and realize automatic registration between infrared and visible images efficiently and precisely on condition that two images to be registered contain abundant corresponding segments and diverse distributions of segment orientations.