提出一种改进Powell法结合遗传算法的混合算法，配准人体安检仪的太赫兹与可见光双波段图像。针对被动式太赫兹图像特征点难以提取的问题，以互信息为相似性测度，通过遗传算法寻优获取7个仿射变换参数的近似解并将其设置为改进Powell算法的初始点，然后利用改进Powell法的局部搜索能力，得到7 个变换参数的精确解。利用单一遗传算法、单一Powell算法和本文混合算法分别对两组太赫兹和可见光图像进行配准。以均方根误差为评价标准对配准结果进行评价。实验结果证明，相比于单一遗传算法和Powell算法，该方法达到了更高的配准精度。能进一步用于太赫兹与可见光双波段图像融合，以提高对衣物内藏匿物品的识别精度，增强太赫兹人体安检仪的实用性。

A hybrid algorithm combining improved Powell algorithm with genetic algorithm is proposed for terahertz and visible dual- band image registration from human security check apparatus. Using mutual information as similarity measure to overcome the difficulty of extracting feature points from passive terahertz images. The approximate solutions of seven affine transformation parameters are obtained by genetic algorithm and set as the initial point of improved Powell algorithm, and then local search capabilities of improved Powell algorithm are used to obtain the exact solutions of seven transformation parameters. Single genetic algorithm, single Powell algorithm and the proposed hybrid algorithm are respectively used to registrate two groups of terahertz and visible images. Root-mean-square error is used as index to evaluate the registration results.The experimental results show that compared with single genetic algorithm and Powell algorithm, the proposed algorithm can achieve higher matching accuracy. The study can be further used for terahertz/visible dual- band image fusion, to improve the recognition accuracy of concealed object in clothes and enhance the practicability of terahertz human security check apparatus.