对于星空观测CCD相机获得的序列图像，由于相机姿态的变化使得图像存在全局运动，空间目标的运动与背景恒星的运动混淆在一起，增加了空间目标检测的难度，为了有效地对空间弱小目标进行检测，提出了一种序列星图配准算法。首先，对图像进行预处理，包括成像传输通道不同导致的辐射不均匀性校正和空间杂散光辐射校正；然后，利用序列图像一一对应的高亮恒星星像质心作为特征点计算全局运动参数；最后，提出了一种基于星像质心坐标矩阵的星图配准算法，将星图转换为星像质心坐标矩阵的形式，利用全局运动参数对星像质心坐标矩阵进行处理，进行星图配准，同时对背景恒星进行滤除。算法能够有效地克服恒星和杂散光的干扰，而且在配准过程中将对星图的处理转化为对星像坐标矩阵的处理，省略了配准过程中的图像重采样与变换步骤，节约大量的运算时间，配准后的图像在对恒星背景进行滤除的同时，在一定程度上降低了弱小目标检测的难度。

For the CCD serial star images, while the space target is moving, the background is still moving because the CCD flat is moving. The global movement of background stars and the local movement of the space target both exist in the star-sky images, which add difficulty to dim target detection. In order to detect dim space target with high efficiency, a star-sky image registration algorithm is put forward. First is the pretreatment, including radiation asymmetry rectification and stray-light rectification. Then, for each frame of image, centroids of the brightest stars are computed. The stars which can radiate in every frame are used as control points to compute the global motion parameters. Finally, the star-sky image is transformed into a form of star centroid coordinate matrix. And the background stars can be eliminated by using the global motion parameters. The algorithm omits image resampling and transformation steps, and thus saves abundant of computing time. The registration image can eliminate the background stars and the dim target can be easily detected from the registration images.