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基于改进大气散射模型的单幅图像去雾方法

杨勇 邱根莹 黄淑英 万伟国 胡威

杨勇, 邱根莹, 黄淑英, 等 . 基于改进大气散射模型的单幅图像去雾方法[J]. 北京航空航天大学学报, 2022, 48(8): 1364-1375. doi: 10.13700/j.bh.1001-5965.2021.0532
引用本文: 杨勇, 邱根莹, 黄淑英, 等 . 基于改进大气散射模型的单幅图像去雾方法[J]. 北京航空航天大学学报, 2022, 48(8): 1364-1375. doi: 10.13700/j.bh.1001-5965.2021.0532
YANG Yong, QIU Genying, HUANG Shuying, et al. Single image dehazing method based on improved atmospheric scattering model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1364-1375. doi: 10.13700/j.bh.1001-5965.2021.0532(in Chinese)
Citation: YANG Yong, QIU Genying, HUANG Shuying, et al. Single image dehazing method based on improved atmospheric scattering model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1364-1375. doi: 10.13700/j.bh.1001-5965.2021.0532(in Chinese)

基于改进大气散射模型的单幅图像去雾方法

doi: 10.13700/j.bh.1001-5965.2021.0532
基金项目: 

国家自然科学基金 61862030

国家自然科学基金 62072218

江西省自然科学基金 20192ACB20002

江西省自然科学基金 20192ACBL21008

详细信息
    通讯作者:

    黄淑英, E-mail: shuyinghuang2010@126.com

  • 中图分类号: TP391.41

Single image dehazing method based on improved atmospheric scattering model

Funds: 

National Natural Science Foundation of China 61862030

National Natural Science Foundation of China 62072218

Natural Science Foundation of Jiangxi, China 20192ACB20002

Natural Science Foundation of Jiangxi, China 20192ACBL21008

More Information
  • 摘要:

    雾天情况下获得的图像通常会出现对比度低、色彩丢失及噪声等问题,传统的去雾方法主要着眼于解决对比度低、色彩损失等问题,而没有考虑空气中灰尘颗粒散射隐藏的噪声光,导致去雾结果中易出现大量的噪声。针对该问题,提出了一种基于改进大气散射模型的单幅图像去雾方法。结合雾霾天气的特点,通过增加空气中介质散射的噪声光对传统雾天成像的大气散射模型进行改进;针对暗通道先验计算透射率不准确的问题,根据改进的模型构建一种透射率精细化的求取方法;结合全变分模型保边抑噪的思想,构造一种新的目标函数,迭代求解获得去雾图像。实验结果和对比分析表明:所提方法能有效去除图像中的雾,减少去雾结果中的噪声,同时也能保留图像中丰富的纹理信息。

     

  • 图 1  本文去雾方法流程

    Figure 1.  Flow chart of the proposed dehazing method

    图 2  透射率及中间结果

    Figure 2.  Transmittance and intermediate results

    图 3  噪声图像去雾结果及中间结果

    Figure 3.  Noise image dehazing results and intermediate results

    图 4  不同δ值的去雾结果

    Figure 4.  Dehazing results with different δ

    图 5  不同γ值的去雾结果

    Figure 5.  Dehazing results with different γ

    图 6  真实有雾图像去雾结果对比

    Figure 6.  Comparison of dehazing results of real hazy images

    图 7  合成有雾图像去雾结果对比

    Figure 7.  Comparison of dehazing results of synthetic hazy images

    表  1  不同方法在HSTS数据集上的SSIM、PSNR、CIEDE2000值对比

    Table  1.   Comparison of SSIM, PSNR and CIEDE2000 values of different methods on HSTS dataset

    方法 SSIM PSNR CIEDE2000
    He[4] 0.738 4 14.86 13.00
    Meng[7] 0.716 0 15.10 12.96
    Berman[8] 0.768 8 17.51 10.68
    Galdran[3] 0.788 9 17.04 11.41
    Shin[24] 0.769 3 17.15 10.65
    Yang[6] 0.800 7 18.21 8.99
    本文方法 0.810 3 18.49 9.42
    注:黑体数据表示最优结果。
    下载: 导出CSV

    表  2  不同方法在SOTS室外数据集上的SSIM、PSNR、CIEDE2000值对比

    Table  2.   Comparison of SSIM, PSNR and CIEDE2000 values of different methods on SOTS outdoor dataset

    方法 SSIM PSNR CIEDE2000
    He[4] 0.753 7 14.65 13.90
    Meng[7] 0.781 9 15.55 12.21
    Berman[8] 0.802 2 18.06 10.25
    Galdran[3] 0.832 0 17.93 10.33
    Shin[24] 0.817 9 17.65 10.07
    Yang[6] 0.827 2 18.59 9.40
    本文方法 0.873 0 19.39 8.26
    注:黑体数据表示最优结果。
    下载: 导出CSV

    表  3  不同方法在SOTS室内数据集上的SSIM、PSNR、CIEDE2000值对比

    Table  3.   Comparison of SSIM, PSNR and CIEDE2000 values of different methods on SOTS indoor dataset

    方法 SSIM PSNR CIEDE2000
    He[4] 0.821 3 16.66 10.73
    Meng[7] 0.793 8 17.04 10.41
    Berman[8] 0.748 8 17.29 11.34
    Galdran[3] 0.781 4 17.52 10.86
    Shin[24] 0.808 9 18.46 9.17
    Yang[6] 0.773 0 16.12 12.40
    本文方法 0.884 8 21.13 6.40
    注:黑体数据表示最优结果。
    下载: 导出CSV

    表  4  SOTS室外数据集上的消融实验

    Table  4.   Ablation experiments on SOTS outdoor dataset

    方法 改进透射率求取 新的目标函数 SSIM PSNR CIEDE2000
    基准 × × 0.753 7 14.65 13.90
    方法1 × 0.868 5 19.38 8.29
    方法2 × 0.813 3 15.76 11.59
    本文方法 0.873 0 19.39 8.26
    注:黑体数据表示最优结果。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-09-17
  • 刊出日期:  2021-09-28

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