深空图像配准是深空图像应用的首要环节。针对目前基于三角形相似性的算法效率低下以及基于星点描述的算法难以应对复杂多变的深空成像环境等问题，提出了一种改进型高效三角形相似法并将其应用到深空图像配准中。首先统计分析了提取星点单像素强度和大小的直方图分布，并将星点划分为稳定星和普通星；然后，将稳定星和普通星分别同最近的两颗稳定星构建三角形；最后，通过引入相似度改进了三角形匹配中的权重矩阵，并提出了衡量三角形相似度的阈值自适应计算方法。实验结果表明，该方法在保证匹配精度的同时极大地提高了深空图像配准的效率，降低了系统资源占用率。

Deep-sky image registration is the most important step in deep-sky image applications. Due to the low efficiency of these existing algorithms based on the triangle similarity, and the hardness of applying algorithms based on star description to deep-sky image registration, an improved efficient triangle similarity algorithm is proposed and applied in the deep-sky image registration. Firstly, the proposed method analyzes the single pixel intensity and star size histograms of detected stars, and divides the stars into stable and normal stars. Then, the triangles are constructed for stable star or normal star with its nearest two stable stars respectively. Finally, by introducing the degree of similarity, the improved voting matrix is used in triangle matching. During this step, an adaptive threshold calculation method is also proposed to measure the triangle similarity. Experimental results show that the proposed method greatly enhances the star matching efficiency, reduces the resources requirement and guarantees the high level of matching precision.