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红外望远镜变步长自动对焦设计

杨鹏博 李洁 崔文楠 张涛

杨鹏博, 李洁, 崔文楠, 张涛. 红外望远镜变步长自动对焦设计[J]. 红外技术, 2021, 43(3): 218-224.
引用本文: 杨鹏博, 李洁, 崔文楠, 张涛. 红外望远镜变步长自动对焦设计[J]. 红外技术, 2021, 43(3): 218-224.
YANG Pengbo, LI Jie, CUI Wennan, ZHANG Tao. Variable Step Autofocus Design for Infrared Telescopes[J]. INFRARED TECHNOLOGY, 2021, 43(3): 218-224.
Citation: YANG Pengbo, LI Jie, CUI Wennan, ZHANG Tao. Variable Step Autofocus Design for Infrared Telescopes[J]. INFRARED TECHNOLOGY, 2021, 43(3): 218-224.

红外望远镜变步长自动对焦设计

详细信息
    作者简介:

    杨鹏博(1996-),男,陕西宝鸡人,硕士生,研究领域为人工智能及数字图像处理,E-mail: yangpb@shanghaitech.edu.cn

    通讯作者:

    崔文楠(1970-),男,研究员,硕士生导师,主要从事空间光电探测技术领域的研究。E-mail: cuiwennan@mail.sitp.ac.cn

    张涛(1966-),男,陕西西安人,研究员,博士生导师,主要从事空间光电探测技术领域的研究。E-mail: haozzh@sina.com

  • 中图分类号: TP391

Variable Step Autofocus Design for Infrared Telescopes

  • 摘要: 在远距离目标检测和跟踪的过程中,成像清晰起着至关重要的作用。红外望远镜系统的成像距离远、景深短、失焦引起图像模糊。由于大气折射,望远镜所成的像处于不断变化之中,造成传统对焦算法对焦成功率、效率偏低。为了提高自动对焦的成功率和速度,采用了一种具备变步长的爬山法,利用多次求图像清晰度取其中位数的方法保证清晰度评价的准确性,利用带动量和加速度的爬山法降低了对焦过程中的不稳定性,减少了粗对焦过程所需的步数。算法在实际中波红外望远镜系统中得到应用,实验结果表明,该算法在粗对焦阶段所需的对焦步数比传统爬山法减少了12.8%,满足红外望远镜系统的需要。
  • 图  1  实际红外望远镜系统

    Figure  1.  Real infrared telescope system

    图  2  红外望远镜自动对焦系统框图

    Figure  2.  Block diagram of auto focusing system for infrared telescopes

    图  3  在静止平台上不同时刻成像对比

    Figure  3.  Imaging comparison at different times on a stationary platform

    图  4  从离焦-准焦-离焦采集的部分图像序列

    Figure  4.  Partial image sequence acquired from defocus to quasi focus to defocus

    图  5  评价函数对采集的图像向最大值归一化后的评价值

    Figure  5.  The evaluation value of the image normalized to the maximum value by the evaluation function

    图  6  对静止目标连续采集成像的评价值

    Figure  6.  Evaluation values of continuous acquisition imaging for stationary targets

    图  7  对静止目标连续采集成像的评价值取中位数作为最终评价值

    Figure  7.  The median is taken as the final evaluation value for the evaluation value of continuous acquisition and imaging of stationary target

    图  8  Tenengrad评价函数中值滤波后的评价函数图

    Figure  8.  Evaluation function graph after median filtering of Tenengrad evaluation function

    图  9  变步长的爬山法算法流程

    Figure  9.  Flowchart of variable step size mountain climbing method algorithm

    图  10  红外望远镜系统自动对焦前图像

    Figure  10.  Infrared telescope system image before autofocus

    图  11  红外望远镜系统自动对焦后图像

    Figure  11.  Infrared telescope system image after autofocus

    表  1  不同对焦评价函数的实时性

    Table  1.   Real time performance of different focusing evaluation functions

    Function Time/ms
    Tenengrad 46.37
    Brener 5.59
    Laplacian 42.68
    Variance 15.55
    Paper[9] 250.9
    Squared 11.64
    Roberts 53.5
    下载: 导出CSV

    表  2  使用对比方法和使用本文提出的变步长自动对焦算法完成对焦所需的步数对比

    Table  2.   The comparison of the number of steps needed to complete the focusing between the compared method and using the variable step size autofocus algorithm proposed in this paper

    Number of out-of-focus pulses Steps needed to finish autofocus
    Contrast algorithm Proposed algorithm
    20 30 40 20 30 40
    100 11.3 9.0 9.0 7.0 8.5 8.5
    200 16.3 11.8 12.3 11.3 11.0 12.8
    300 21.3 17.0 15.3 14.0 14.0 14.5
    400 25.7 19.0 18.0 17.8 15.0 15.8
    下载: 导出CSV

    表  3  使用两种算法对焦前后的红外图像清晰度对比(其中Tenengrad数值均为原数值除以1×109后的结果)

    Table  3.   The comparison of clarity of the infrared image between the two methods(where all the Tenengrad values were divided by 1×109)

    Number of out-of-focus pulses Tenengrad value before autofocus Tenengrad value after autofocus
    Contrast algorithm Clarity improved Proposed algorithm Clarity improved
    100 7.6771 9.4609 23.24% 9.3857 22.26%
    200 6.7049 9.3238 39.06% 9.3512 39.47%
    300 6.1801 9.6894 56.78% 9.4935 53.61%
    400 5.8859 9.3488 58.83% 9.8968 68.14%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-21
  • 修回日期:  2020-12-30
  • 刊出日期:  2021-04-02

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