结合相位特征与边缘特征的红外与可见光图像配准算法

Infrared and Visible Image Registration Algorithm Combining Phase Feature and Edge Feature

  • 摘要: 红外与可见光图像存在显著的非线性辐射差异,导致传统的配准算法精确度不高、鲁棒性不强。本文结合相位与边缘特征提出了一种精确、鲁棒的配准算法。为提高特征点准确性,通过叠加相位一致性矩特征构建叠加矩图,然后在叠加矩图上均匀提取特征点。由于单一特征来源的描述符描述能力有限,本文结合相位特征与边缘特征构建特征描述符,其中相位特征基于多尺度最大索引图提取,边缘特征基于叠加矩图提取,最后经过特征匹配得到图像之间的变换关系完成配准。实验表明,提出的算法与对比方法相比在正确匹配点数、正确匹配率和精度等方面都有显著提升。

     

    Abstract: Significant nonlinear radiation differences exist between infrared and visible images, resulting in low accuracy and poor robustness in traditional registration algorithms. To address this issue, a robust registration algorithm combining phase and edge features was proposed. To improve the accuracy of feature points, a phase-consistent superimposed moment map was established, and feature points were uniformly extracted from this map. Due to the limited descriptive power of descriptors derived from a single feature source, a descriptor combining both phase and edge features was constructed. Phase features were extracted based on a multiscale maximum index map, while edge features were extracted from the superimposed moment map. For registration, feature matching is performed to obtain the transformation relationship between the images. Experiments demonstrate that the proposed algorithm significantly improves the number of correct matching points, the correct matching rate, and overall accuracy compared to other methods.

     

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