Local adaptive threshold segmentation method for subapture spots of Shack-Hartmann sensor

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.