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

Li Xuxu; Li Xinyang; Wang Caixia

doi:10.12086/oee.2018.170699

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Li Xuxu, Li Xinyang, Wang Caixia. Local adaptive threshold segmentation method for subapture spots of Shack-Hartmann sensor[J]. Opto-Electronic Engineering, 2018, 45(10): 170699. doi: 10.12086/oee.2018.170699

Citation:

Li Xuxu, Li Xinyang, Wang Caixia. Local adaptive threshold segmentation method for subapture spots of Shack-Hartmann sensor[J]. Opto-Electronic Engineering, 2018, 45(10): 170699. doi: 10.12086/oee.2018.170699

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

1.
Key Laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
2.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Fund Project: Supported by National Natural Science Foundation of China (61505215)

More Information

^*Corresponding author: Li Xinyang, E-mail:xyli@ioe.ac.cn

Received Date 19 December 2017

Revised Date 10 April 2018

Published Date 01 October 2018

Abstract

Abstract

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 SNR_p between 10~40 conditions.
- Shack-Hartmann sensor /
- point source spots /
- local adaptive threshold /
- centroiding /
- centre of gravity

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References

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Overview

Overview

Overview: 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. A globally estimated threshold using the best threshold method (mean of noise plus three times of its standard deviation) ignores the difference between subaptures, thus causing large centroiding estimation error for subaptures with higher noise level. Therefore we propose an adaptive threshold segmentation method based on statistical rank, which can reduce the influence of background noise effectively. The pixels within a subapture is ranked by their intensities at first. The mean and standard deviation of the subapture noise is estimated using the last certain numbers of pixels. The number of pixels used for noise estimation is determined by estimating the size of Shack-Hartmann spots, which is related to the focal length, the wavelength, the diameter of micro lens and the size of pixel.
An experiment measuring static aberration was conducted, the accuracy of centroid estimation and wavefront reconstruction both testify the effectiveness of this method. Different from theoretical simulations, the ideal position of a spot is unknown in real experiments. However we have two ways to evaluate the accuracy of centroiding methods. Firtly, the actual postion of a certain subapture is constant for static aberrations, and the variation of centroiding for a subapture within multiple frames can be calculated and used as a criteria. Another is that we calculate the center of a spot under high signal-to-noise ratio (SNR) as the ideal position, which can be used to estimate the errors under low SNR conditions. Since the intensity of a subapture increases with the exposure time, we controlled the signal-to-noise ratio by adjusting the exposure time of the camera, which was set as 10 ms, 5 ms, 2 ms and 1 ms. Furthermore, the wavefront reconstruction errors (PV and RMS) had been calculated and displayed within this paper.
We also found that combing adaptive threshold method with intensity weighted centroiding (IWC) method can improve the performance of traditional centre of gravity method. It achieves higher centroiding accuracy under low SNR conditions (10 < SNR_p < 40), comparing with the traditional method. Although several methods have been proposed to improve the CoG method, such as using Gaussian weighting function or window, the center of the weighting function or the window is difficult to define at first. However, IWC method can avoid this problem by simply using the intensities of the spot itself and the choice of parameter is much more flexible and easy.