Chinese Journal of Lasers, Volume. 51, Issue 22, 2210001(2024)

Simulation Analysis of Correlating Hartman Wavefront Detection Based on Feature Matching

Linxiong Wen1,2,3, Yi Tan1,2、*, Ping Yang1,2, and Wang Zhao1,2
Author Affiliations
  • 1National Laboratory on Adaptive Optics, Chengdu 610209, Sichuan , China
  • 2Institute of Optoelectronic Technology, Chinese Academy of Sciences, Chengdu 610209, Sichuan , China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    Figures & Tables(13)
    Extension targets with different extension degrees. (a) Triangular; (b) heart; (c) five-point star; (d) smiling face; (e) tree; (f) fire balloon; (g) four-wing aircraft; (h) six-wing aircraft
    Six-wing aircraft with different θ. (a) θ=0; (b) θ=0.1; (c) θ=0.3; (d) θ=0.5
    Influence of partial missing of sub-aperture image on cross-correlation algorithm. (a) Extended target; (b) test-subimage;
    SURF feature matching preprocessing of partial missing sub-aperture images. (a) Extended target; (b) test-subimage;
    Optical path diagram of experiment
    Three sets of input aberrations to be measured. (a)‒(c) Generated three different sets of aberrations to be measured;
    Slope extraction error of objects with different extension degrees
    Slope extraction process based on SURF feature matching preprocessing
    Slope extraction error of extended objects when degree of missing is different. (a) Five point star; (b) four-wing aircraft ; (c) six-wing aircraft
    Effects of system resolution and SNR on slope extraction errors of SURF feature matching preprocessing. (a) Resolution is 48 pixel×48 pixel; (b) resolution is 32 pixel×32 pixel; (c) SNR is 20 dB; (d) SNR is 10 dB
    Schematic diagram of correlated Hartman sub-apertures. (a) Schematic of image partially missing; (b) ideal imaging target
    Extended object wave-front restoration results based on SURF feature matching preprocessing. (a1)(b1)(c1) When θ=0.4, wavefront recovery coefficient based on original cross-correlation of corresponding wavefront to be measured; (a2)(b2)(c2) when θ=0.4, cross-correlated wavefront recovery coefficient of corresponding wavefront to be measured based on SURF feature extraction; (a3)(b3)(c3) when θ=0.6, wavefront recovery coefficient based on original cross-correlation of corresponding wavefront to be measured is obtained; (a4)(b4)(c4) when θ=0.6, cross-correlated wavefront recovery coefficient of corresponding wavefront to be measured based on SURF feature extraction
    • Table 1. Extension degree of each extended objects

      View table

      Table 1. Extension degree of each extended objects

      Extended objectJCnCrEEd
      Triangular0.220.310.150.210.15
      Heart0.430.540.460.670.32
      Five-point star0.530.760.240.590.58
      Smiling face0.20.560.350.730.74
      Tree0.130.340.641.230.80
      Fire balloon0.700.570.391.340.82
      Four-wing aircraft0.690.700.871.710.84
      Six-wing aircraft1.971.820.802.161.21
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    Linxiong Wen, Yi Tan, Ping Yang, Wang Zhao. Simulation Analysis of Correlating Hartman Wavefront Detection Based on Feature Matching[J]. Chinese Journal of Lasers, 2024, 51(22): 2210001

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    Paper Information

    Category: remote sensing and sensor

    Received: Jan. 10, 2024

    Accepted: Mar. 4, 2024

    Published Online: Nov. 13, 2024

    The Author Email: Tan Yi (tanyiustc@163.com)

    DOI:10.3788/CJL240482

    CSTR:32183.14.CJL240482

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