方向相关与互信息加权组合多模图像配准方法

Multi-model image registration based on weighted orientation correlation and mutual information

  • 摘要: 针对可见光与红外图像的配准问题,提出了一种基于边缘方向相关和局部优选区域互信息加权的配准方法。首先进行区域划分,建立区域优选测度,选取信息量丰富的局部区域;在优选区域的基础上,引入了图像的边缘方向信息,构造边缘方向图,以增加全局空间特征;然后,通过综合局部区域灰度信息和全局边缘方向信息,将互相关测度和互信息测度加权集成,构造了一种新的相似测度。实验结果表明,边缘方向相关测度的引入,提高了基于互信息的图像配准的精度,利用局部优选区域代替整幅图像提高了算法的速度。

     

    Abstract: An improved multi-model image registration method was proposed for visible and infrared image registration, which was based on optimal region mutual information (MI) and edge orientation correlation (OC). Firstly, with proper principle, local regions with rich information were selected by image partition. Then, in order to include spatial information, image edge orientation was introduced to construct an edge orientation map. Finally, by integrating the correlation of edge orientation map and MI of optimal regions with a trade-off weight, a new similarity metric was constructed. Experiment results show that by combining edge orientation correlation, this new metric can effectively improve the accuracy of MI based method, and processing with local optimal regions instead of whole image can improve the efficiency.

     

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