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

    Fund Project: Supported by National Natural Science Foundation of China (61505215)
More Information
  • 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.
  • 加载中
  • [1] Lukin V P, Botygina N N, Emaleev O N, et al. Wavefront sensors for adaptive optical systems[J]. Proceedings of SPIE, 2010, 7828: 78280P. doi: 10.1117/12.865964

    CrossRef Google Scholar

    [2] Vargas J, González-Fernandez L, Quiroga J A, et al. Shack-Hartmann centroid detection method based on high dynamic range imaging and normalization techniques[J]. Applied Optics, 2010, 49(13): 2409-2416. doi: 10.1364/AO.49.002409

    CrossRef Google Scholar

    [3] 饶长辉, 朱磊, 张兰强, 等.太阳自适应光学技术进展[J].光电工程, 2018, 45(3): 170733. doi: 10.12086/oee.2018.170733

    CrossRef Google Scholar

    Rao C H, Zhu L, Zhang L Q, et al. Development of solar adaptive optics[J]. Opto-Electronic Engineering, 2018, 45(3): 170733. doi: 10.12086/oee.2018.170733

    CrossRef Google Scholar

    [4] 姜文汉.自适应光学发展综述[J].光电工程, 2018, 45(3): 170489. doi: 10.12086/oee.2018.170489

    CrossRef Google Scholar

    Jiang W H. Overview of adaptive optics development[J]. Opto-Electronic Engineering, 2018, 45(3): 170489. doi: 10.12086/oee.2018.170489

    CrossRef Google Scholar

    [5] Ares J, Arines J. Effective noise in thresholded intensity distribution: influence on centroid statistics[J]. Optics Letters, 2001, 26(23): 1831-1833. doi: 10.1364/OL.26.001831

    CrossRef Google Scholar

    [6] 沈锋, 姜文汉.提高Hartmann波前传感器质心探测精度的阈值方法[J].光电工程, 1997, 24(3): 1-8.

    Google Scholar

    Shen F, Jiang W H. A method for improving the centroid sensing accuracy threshold of Hartmann wavefront sensor[J]. Opto-Electronic Engineering, 1997, 24(3): 1-8.

    Google Scholar

    [7] Ma X Y, Rao C H, Zheng H Q. Error analysis of CCD-based point source centroid computation under the background light[J]. Optics Express, 2009, 17(10): 8525-8541. doi: 10.1364/OE.17.008525

    CrossRef Google Scholar

    [8] Thomas S, Fusco T, Tokovinin A, et al. Comparison of centroid computation algorithms in a Shack-Hartmann sensor[J]. Monthly Notices of the Royal Astronomical Society, 2006, 371(1): 323-336. doi: 10.1111/j.1365-2966.2006.10661.x

    CrossRef Google Scholar

    [9] Yin X M, Li X, Zhao L P, et al. Adaptive thresholding and dynamic windowing method for automatic centroid detection of digital Shack-Hartmann wavefront sensor[J]. Applied Optics, 2009, 48(32): 6088-6098. doi: 10.1364/AO.48.006088

    CrossRef Google Scholar

    [10] Vyas A, Roopashree M B, Prasad B R. Centroid detection by Gaussian pattern matching in adaptive optics[J]. International Journal of Computer Applications, 2010, 1(26): 32-37. doi: 10.5120/ijca

    CrossRef Google Scholar

    [11] Baker K L, Moallem M M. Iteratively weighted centroiding for Shack-Hartmann wave-front sensors[J]. Optics Express, 2007, 15(8): 5147-5159. doi: 10.1364/OE.15.005147

    CrossRef Google Scholar

    [12] 任剑峰, 饶长辉, 李明全.一种Hartmann-Shack波前传感器图像的自适应阈值选取方法[J].光电工程, 2002, 29(1): 1-5. doi: 10.3969/j.issn.1003-501X.2002.01.001

    CrossRef Google Scholar

    Ren J F, Rao C H, Li M Q. An adaptive threshold selection method for Hartmann-Shack wavefront sensor[J]. Opto-Electronic Engineering, 2002, 29(1): 1-5. doi: 10.3969/j.issn.1003-501X.2002.01.001

    CrossRef Google Scholar

    [13] Li X X, Li X Y, Wang C X. Optimum threshold selection method of centroid computation for Gaussian spot[J]. Proceedings of SPIE, 2015, 9675: 967517. doi: 10.1117/12.2199247

    CrossRef Google Scholar

  • 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 < SNRp < 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.

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(7)

Tables(3)

Article Metrics

Article views(7242) PDF downloads(3268) Cited by(0)

Access History

Other Articles By Authors

Article Contents

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint