基于梯度与归一化互信息的印刷图像配准

目的为了提高印刷图像配准的精度。方法提出一种基于梯度和归一化互信息的印刷图像配准方法。首先获取图像的归一化互信息,同时使用边缘检测算子获取图像边缘梯度的模值和方向角,然后根据边缘梯度信息和归一化互信息构造新的测度函数,以新的测度函数为目标函数,用Powell优化算法获取用于配准的最优参数。结果通过将文中提出的方法和基于归一化互信息的图像配准方法分别用于印刷图像配准,得到的统计实验结果表明,对于100项随机配准参数,新方法得出的配准误差波动幅度更小,配准精度更高。结论文中所提方法在准确性上优于基于归一化互信息的图像配准方法。

Printing Image Registration Based on Gradient and Normalized Mutual Information

The work aims to improve the accuracy of printing image registration (IR). A printing image registration method based on gradient and normalized mutual information (NMI) was proposed. The NMI was firstly obtained, and the modulus and direction angle of the image edge gradient were obtained with the edge detection operator. Then, a new measure function was designed based on the edge gradient information and NMI. The Powell was used to optimize the algorithm to obtain the best parameters for registration with the new measure function as the objective function. The proposed method and the NMI-based image registration method were respectively used for printing IR. The results obtained from the statistical experiments showed that, the new method had smaller fluctuation range of registration errors and higher registration accuracy regarding 100 random registration parameters. The proposed method is superior to the NMI-based image registration method in terms of accuracy.