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火星沙尘环境光学图像增强方法

李海波 曹云峰 丁萌 庄丽葵

李海波, 曹云峰, 丁萌, 等 . 火星沙尘环境光学图像增强方法[J]. 北京航空航天大学学报, 2018, 44(3): 444-453. doi: 10.13700/j.bh.1001-5965.2017.0188
引用本文: 李海波, 曹云峰, 丁萌, 等 . 火星沙尘环境光学图像增强方法[J]. 北京航空航天大学学报, 2018, 44(3): 444-453. doi: 10.13700/j.bh.1001-5965.2017.0188
LI Haibo, CAO Yunfeng, DING Meng, et al. Optical image enhancement method in dust environment on Mars[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(3): 444-453. doi: 10.13700/j.bh.1001-5965.2017.0188(in Chinese)
Citation: LI Haibo, CAO Yunfeng, DING Meng, et al. Optical image enhancement method in dust environment on Mars[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(3): 444-453. doi: 10.13700/j.bh.1001-5965.2017.0188(in Chinese)

火星沙尘环境光学图像增强方法

doi: 10.13700/j.bh.1001-5965.2017.0188
基金项目: 

国家自然科学基金 61673211

江苏省研究生培养创新工程 KYLX_0282

详细信息
    作者简介:

    李海波  男, 博士研究生。主要研究方向:深空探测GNC技术、计算机视觉

    曹云峰  男,教授,博士生导师。主要研究方向:深空探测GNC技术、无人机飞行控制与导航、计算机视觉

    通讯作者:

    曹云峰. E-mail: cyfac@nuaa.edu.cn

  • 中图分类号: V19

Optical image enhancement method in dust environment on Mars

Funds: 

National Natural Science Foundation of China 61673211

Funding of Jiangsu Innovation Program for Graduate Education KYLX_0282

More Information
  • 摘要:

    针对火星探测器着陆时沙尘天气对机器视觉的影响,提出一种去除沙尘天气对光学成像影响的方法,为视觉系统提供清晰输入图像。首先对受沙尘天气影响的图像建立模型,然后求取模型中大气光值与透射系数值。对于大气光值的计算采用基于四叉树细分的方法,在最小值图像上搜寻指定阈值面积中灰度均值最大的区域,在初始图像中相同区域计算各通道均值,作为大气光值。在此基础上计算透射系数,完成清晰图像的恢复。通过对受沙尘影响图像测试表明,该方法能够将受沙尘影响的图像恢复成清晰的图像。即使在复杂的环境中,该方法对光照变化、沙尘强度变化和场景变化等仍具有较好的效果。与其他方法相比,本文方法在去除沙尘对光学图像影响方面效果较好,在恢复图像评价指标等方面优于其他方法,能够进一步提高图像清晰度,为光学图像的后期处理提供更丰富信息。

     

  • 图 1  受沙尘影响的图像

    Figure 1.  Images affected by dust

    图 2  最小值图像

    Figure 2.  Minimum images

    图 3  细分搜寻结果

    Figure 3.  Subdivision search results

    图 4  搜寻结果在初始图位置

    Figure 4.  Search results on the positions of initial images

    图 5  透射系数图

    Figure 5.  Transmission coefficient maps

    图 6  优化后的透射系数图

    Figure 6.  Optimized transmission coefficient maps

    图 7  最终恢复图像

    Figure 7.  Final recovered images

    图 8  初始受沙尘影响图像RGB通道直方图

    Figure 8.  RGB channel histograms of initial dust images

    图 9  最终恢复图像RGB通道直方图

    Figure 9.  RGB channel histograms of final recovered images

    图 10  受沙尘影响图像及设置不同阈值的测试结果

    Figure 10.  Dust image and test results with different threshold values

    图 11  不同光照条件下受沙尘影响图像及对应恢复图像

    Figure 11.  Images affected by dust in different illumination conditions and corresponding recovered images

    图 12  不同沙尘强度下受沙尘影响图像及对应恢复图像

    Figure 12.  Images affected by dust with different intensities and corresponding recovered images

    图 13  不同场景下受沙尘影响图像及对应恢复图像

    Figure 13.  Images affected by dust in different scenarios and corresponding recovered images

    图 14  受沙尘影响图像及各种方法恢复结果对比

    Figure 14.  Image affected by dust and comparison of recovery results among various methods

    表  1  定量分析对比

    Table  1.   Comparison of quantitative analysis

    客观评价指标 初始图像 MSR SSR MSRCR 本文方法
    清晰度 3.0222 6.5353 6.5319 6.5368 12.2851
    标准差 12.4507 25.6661 25.7078 25.1149 38.3383
    下载: 导出CSV

    表  2  边缘强度对比

    Table  2.   Comparison of edge intensity

    客观评价指标 初始图像 MSR SSR MSRCR 本文方法
    边缘强度 24.3585 51.88445 51.8465 51.7509 96.1843
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-03-29
  • 录用日期:  2017-05-05
  • 刊出日期:  2018-03-20

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