夏克-哈特曼传感器的质心偏移估计精度受噪声的影响非常大，在传统质心法(CoG)中尤为突出，因而阈值的选取十分重要。本文提出了一种基于统计排序的局部自适应阈值分割方法，并与传统的全局阈值法进行对比，发现自适应的局部阈值能够更加有效地分割出阵列光斑，从而减小背景噪声对质心估计的影响，降低波面复原误差。本文通过静态相差的测量实验，从质心偏移估计的精度和波前复原精度两个方面进行分析，验证了该方法的有效性。另外，本文发现自适应阈值结合灰度加权的质心提取方法，是对传统质心法的较好改进，可以有效提高峰值信噪比大约10～40的光斑质心提取精度。

The accuracy of centroid estimation for Shcak-Hartmann wavefront sensor is highly dependent on noise, especially for the centre of gravity (CoG) method. Therefore, threshold selection is very important. This paper proposes a local adaptive threshold segmentation method based on statistical rank, which can reduce the influence of uneven background noise and decrease the wavefront reconstruction error more effectively, comparing with the traditional global threshold method. An experiment measuring static aberration was conducted, the accuracy of centroid estimation and wavefront reconstruction both testify the effectiveness of this method. Besides, we found that combing the local adaptive threshold method and intensity weighted centroiding (IWC) method can improve the performance of traditional centre of gravity method. It achieves higher centroiding accuracy under SNRp between 10~40 conditions.