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基于多尺度特征结合细节恢复的单幅图像去雾方法

张世辉 路佳琪 宋丹丹 张晓微

张世辉, 路佳琪, 宋丹丹, 张晓微. 基于多尺度特征结合细节恢复的单幅图像去雾方法[J]. 电子与信息学报, 2022, 44(11): 3967-3976. doi: 10.11999/JEIT210868
引用本文: 张世辉, 路佳琪, 宋丹丹, 张晓微. 基于多尺度特征结合细节恢复的单幅图像去雾方法[J]. 电子与信息学报, 2022, 44(11): 3967-3976. doi: 10.11999/JEIT210868
ZHANG Shihui, LU Jiaqi, SONG Dandan, ZHANG Xiaowei. Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3967-3976. doi: 10.11999/JEIT210868
Citation: ZHANG Shihui, LU Jiaqi, SONG Dandan, ZHANG Xiaowei. Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3967-3976. doi: 10.11999/JEIT210868

基于多尺度特征结合细节恢复的单幅图像去雾方法

doi: 10.11999/JEIT210868
基金项目: 中央引导地方科技发展资金项目(216Z0301G),河北省自然科学基金(F2019203285)
详细信息
    作者简介:

    张世辉:男,教授,博士生导师,研究方向为视觉信息处理、模式识别

    路佳琪:女,硕士生,研究方向为雾天图像处理、计算机视觉

    宋丹丹:女,硕士生,研究方向为对抗样本攻防、计算机视觉

    张晓微:男,硕士生,研究方向为对抗样本攻防、计算机视觉

    通讯作者:

    路佳琪 1078271268@qq.com

  • 中图分类号: TN911.73; TP391

Single Image Dehazing Method Based on Multi-scale Features Combined with Detail Recovery

Funds: The Central Government Guided Local Funds for Science and Technology Development (216Z0301G), The Natural Science Foundation of Hebei Province (F2019203285)
  • 摘要: 为提高单幅图像去雾方法的准确性及其去雾结果的细节可见性,该文提出一种基于多尺度特征结合细节恢复的单幅图像去雾方法。首先,根据雾在图像中的分布特性及成像原理,设计多尺度特征提取模块及多尺度特征融合模块,从而有效提取有雾图像中与雾相关的多尺度特征并进行非线性加权融合。其次,构造基于所设计多尺度特征提取模块和多尺度特征融合模块的端到端去雾网络,并利用该网络获得初步去雾结果。再次,构造基于图像分块的细节恢复网络以提取细节信息。最后,将细节恢复网络提取出的细节信息与去雾网络得到的初步去雾结果融合得到最终清晰的去雾图像,实现对去雾后图像视觉效果的增强。实验结果表明,与已有代表性的图像去雾方法相比,所提方法能够对合成图像及真实图像中的雾进行有效去除,且去雾结果细节信息保留完整。
  • 图  1  所提单幅图像去雾方法流程

    图  2  MSFEM结构

    图  3  MSFFM结构

    图  4  去雾网络的整体结构

    图  5  基础块结构

    图  6  细节恢复网络的整体结构

    图  7  消融实验结果比较

    图  8  本文方法与已有方法在合成图像上的比较

    图  9  本文方法与已有方法在真实图像上的比较

    表  1  不同配置消融实验的PSNR和SSIM指标对比

    数据集MSFEMMSFEM+MSFFMMSFEM+MSFFM+细节恢复网络
    PSNR$ \uparrow $SSIM$ \uparrow $PSNR$ \uparrow $SSIM$ \uparrow $PSNR$ \uparrow $SSIM$ \uparrow $
    SOTS(Indoor)33.060.97733.520.97935.160.988
    SOTS(Outdoor)29.110.96632.640.97933.780.985
    Middlebury16.530.84016.610.84317.480.864
    下载: 导出CSV

    表  2  各算法的PSNR和SSIM指标对比

    数据集客观指标DCP[9]DeHazeNet[13]AOD-Net[14]GridDehazeNet[16]FFA-Net[18]MSBDN[25]所提方法
    SOTS(Indoor)PSNR$ \uparrow $16.6119.8220.5132.1636.3932.5135.16
    SSIM$ \uparrow $0.8550.8210.8160.9840.9890.9770.988
    SOTS(Outdoor)PSNR$ \uparrow $19.1424.7524.1430.8633.5733.6433.78
    SSIM$ \uparrow $0.8610.9270.9200.9820.9840.9820.985
    MiddleburyPSNR$ \uparrow $11.9413.9416.9413.7617.3417.48
    SSIM$ \uparrow $0.7620.7430.8560.6980.8620.864
    I-HAZEPSNR$ \uparrow $11.9914.5815.3416.5315.2623.9320.73
    SSIM$ \uparrow $0.5280.6880.7040.7680.6960.8910.813
    O-HAZEPSNR$ \uparrow $13.2715.7716.4516.7521.3624.3625.80
    SSIM$ \uparrow $0.5760.6970.6840.7660.8690.7490.875
    下载: 导出CSV

    表  3  不同算法的运行时间和参数量对比

    GridDehazeNet[16]FFA-Net[18]MSBDN[25]所提方法
    运行时间(s)0.320.330.050.22
    参数量(M)0.964.4631.354.89
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-23
  • 录用日期:  2021-12-14
  • 修回日期:  2021-12-13
  • 网络出版日期:  2021-12-27
  • 刊出日期:  2022-11-14

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