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基于图像增强的无人机侦察图像去雾方法

黄宇晴 丁文锐 李红光

黄宇晴, 丁文锐, 李红光等 . 基于图像增强的无人机侦察图像去雾方法[J]. 北京航空航天大学学报, 2017, 43(3): 592-601. doi: 10.13700/j.bh.1001-5965.2016.0169
引用本文: 黄宇晴, 丁文锐, 李红光等 . 基于图像增强的无人机侦察图像去雾方法[J]. 北京航空航天大学学报, 2017, 43(3): 592-601. doi: 10.13700/j.bh.1001-5965.2016.0169
HUANG Yuqing, DING Wenrui, LI Hongguanget al. Haze removal method for UAV reconnaissance images based on image enhancement[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 592-601. doi: 10.13700/j.bh.1001-5965.2016.0169(in Chinese)
Citation: HUANG Yuqing, DING Wenrui, LI Hongguanget al. Haze removal method for UAV reconnaissance images based on image enhancement[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(3): 592-601. doi: 10.13700/j.bh.1001-5965.2016.0169(in Chinese)

基于图像增强的无人机侦察图像去雾方法

doi: 10.13700/j.bh.1001-5965.2016.0169
基金项目: 

国家自然科学基金 61601014

详细信息
    作者简介:

    黄宇晴, 女, 硕士研究生。主要研究方向:图像处理

    李红光, 男, 博士, 工程师。主要研究方向:图像处理

    通讯作者:

    李红光, E-mail:lihongguang@buaa.edu.cn

  • 中图分类号: TP391

Haze removal method for UAV reconnaissance images based on image enhancement

Funds: 

National Natural Science Foundation of China 61601014

More Information
  • 摘要:

    针对无人机(UAV)雾霾天气下的侦察图像,并考虑无人机自身特性,提出了一种新的基于图像增强的无人机侦察图像去雾方法。该方法从图像处理的角度,通过对图像分别进行白平衡处理和对比度增强处理,基于图像融合和自动色阶处理,最终得到复原图像。选取无人机侦察图像进行去雾,并从主观和客观对实验结果进行评价,本方法得到的去雾图像的图像评价指标均有明显提高;同时与其他典型的去雾方法相比,综合评价指标提升,证明本方法可以得到良好的去雾效果。

     

  • 图 1  本文去雾方法流程图

    Figure 1.  Proposed dehazing method flowchart

    图 2  大气散射模型示意图

    Figure 2.  Schematic diagram of atmospheric scattering model

    图 3  自动色阶算法流程图

    Figure 3.  Auto levels algorithm flowchart

    图 4  不同方法的去雾效果

    Figure 4.  Dehazing results of different methods

    图 5  综合评价指标比较

    Figure 5.  Comparison of comprehensive evaluation index

    表  1  图像融合权重对应雾浓度级别

    Table  1.   Image fusion weights and their corresponding grades of haze concentration

    雾气浓度级别 对比度权重 白平衡权重
    0 0 1
    1 0.2 0.8
    2 0.4 0.6
    3 0.6 0.4
    4 0.8 0.2
    下载: 导出CSV

    表  2  去雾前后的标准差和信息熵

    Table  2.   Standard deviation and information entropy before and after dehazing

    序号 标准差 信息熵
    原图 去雾后 原图 去雾后
    1 21.817 700 55.956 15 4.114 371 5.231 381
    2 23.575 430 53.351 33 3.709 058 4.910 504
    3 9.018 065 43.797 79 3.350 642 5.034 718
    4 6.289 762 30.208 87 3.024 568 4.867 457
    5 16.996 810 36.074 45 4.505 224 5.264 960
    6 10.341 110 23.898 56 3.451 581 4.779 376
    7 18.149 810 41.873 90 4.587 965 5.440 413
    下载: 导出CSV

    表  3  去雾前后的CNI和CCI

    Table  3.   CNI and CCI before and after dehazing

    序号 CNI CCI
    原图 去雾后 原图 去雾后
    1 0.458 457 0.564 170 65.952 55 167.536 70
    2 0.545 394 0.624 008 100.782 20 157.108 10
    3 0.463 714 0.597 543 45.798 24 162.026 30
    4 0.450 407 0.522 387 38.751 14 136.424 70
    5 0.435 621 0.520 872 23.266 67 48.885 40
    6 0.422 680 0.417 078 12.794 68 27.341 50
    7 0.468 173 0.492 878 26.322 86 40.960 57
    下载: 导出CSV

    表  4  不同方法的综合评价指标比较

    Table  4.   Comparison of comprehensive evaluation index among different methods

    序号 原图 (a) 自适应直方图均衡 (b) 自适应色阶对比度 (c) 多尺度Retinex (d) 均值滤波 (e) 高斯滤波 (f) 暗通道先验 (g) 本文方法 (h)
    1 1.001 3 1.935 004 2.522 596 2.076 970 2.174 849 2.384 549 2.831 008 2.542 759
    2 1.096 2 2.180 398 3.255 074 2.170 184 1.738 967 2.488 135 2.566 417 2.636 486
    3 0.398 5 1.167 745 1.740 431 2.200 087 0.486 920 1.058 386 2.026 381 2.268 815
    4 0.171 5 0.618 922 1.463 787 2.015 525 0.242 398 0.994 793 2.240 572 1.948 523
    5 0.931 4 1.983 790 2.130 797 2.185 008 1.619 569 1.962 495 2.030 415 2.020 827
    6 0.319 6 1.073 086 1.533 132 2.008 388 0.676 075 0.858 428 0.910 497 1.157 127
    7 1.050 4 2.015 479 2.038 903 2.097 325 2.669 215 2.004 094 2.214 031 2.440 271
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-03-07
  • 录用日期:  2016-04-29
  • 网络出版日期:  2017-03-20

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