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颜色一致性的无人机航拍低光照图像增强算法

王殿伟 刘旺 房杰 许志杰

欧阳平超, 焦宗夏, 刘红梅等 . 分布式液压流体脉动主动控制方法[J]. 北京航空航天大学学报, 2007, 33(09): 1060-1063.
引用本文: 王殿伟,刘旺,房杰,等. 颜色一致性的无人机航拍低光照图像增强算法[J]. 北京航空航天大学学报,2025,51(4):1096-1106 doi: 10.13700/j.bh.1001-5965.2023.0172
Ouyang Pingchao, Jiao Zongxia, Liu Hongmeiet al. Study on distributed active control of fluid pulsation in hydraulic piping[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(09): 1060-1063. (in Chinese)
Citation: WANG D W,LIU W,FANG J,et al. Low illumination image enhancement algorithm for UAV aerial photography with color consistency[J]. Journal of Beijing University of Aeronautics and Astronautics,2025,51(4):1096-1106 (in Chinese) doi: 10.13700/j.bh.1001-5965.2023.0172

颜色一致性的无人机航拍低光照图像增强算法

doi: 10.13700/j.bh.1001-5965.2023.0172
基金项目: 国家自然科学基金青年基金(62201454); 陕西省国际科技合作计划(2023-GHYB-04)
详细信息
    通讯作者:

    E-mail:wangdianwei@xupt.edu.cn

  • 中图分类号: TP391.41

Low illumination image enhancement algorithm for UAV aerial photography with color consistency

Funds: Youth Fund of the National Natural Science Foundation of China (62201454); Shaanxi Provincial International Science and Technology Cooperation Plan (2023-GHYB-04)
More Information
  • 摘要:

    针对无人机(UAV)在低光照条件下拍摄的图像存在亮度低、视觉效果差等问题,建立了无人机航拍低光照数据集,提出了一种颜色一致性的无人机航拍低光照图像增强算法。在亮度增强阶段,构建亮度增强网络(BENet)来增强图像的亮度,利用颜色学习网络(CNet)模块和金字塔颜色嵌入(PCE)模块将图像的颜色特征和内容特征相结合,避免增强后的图像出现颜色失真;在图像校正阶段,构建基于域转移的校正网络,使用自建的数据集训练网络,借助光照良好图像来校正亮度增强阶段增强后的图像,减少噪声对图像的影响,得到增强后的图像。实验结果表明:所提算法可提升图像亮度,避免出现噪声放大和颜色失真的问题,在客观指标上,整体优于对比算法,同时还能提升目标检测算法在夜间的检测性能。

     

  • 图 1  本文网络结构

    Figure 1.  Structure of the proposed network

    图 2  PCE模块结构

    Figure 2.  Structure of PCE module

    图 3  数据集图像

    Figure 3.  Dataset images

    图 4  不同算法在建筑场景下的增强结果

    Figure 4.  Enhancement results of different algorithms in architectural scenarios

    图 5  不同算法在街道场景下的增强结果

    Figure 5.  Enhancement results of different algorithms in street scenarios

    图 6  不同算法在野外场景下的增强结果

    Figure 6.  Enhancement results of different algorithms in field scenarios

    图 7  不同算法在公开数据集场景下的增强结果

    Figure 7.  Enhancement results of different algorithms on public datasets

    图 8  颜色一致性消融实验对比

    Figure 8.  Comparison of color consistency ablation experiments

    图 9  图像校正去噪消融实验对比

    Figure 9.  Comparison of image correction ablation experiments

    图 10  检测算法结果

    Figure 10.  Detection algorithm results

    表  1  无人机航拍低光照数据集

    Table  1.   Low illumination dataset of UAV photography

    场景 图像数量/张
    建筑 4000
    街道 4000
    野外 2000
    下载: 导出CSV

    表  2  不同算法在自建数据集上的客观评价指标

    Table  2.   Objective evaluation indicators for different algorithms on self-built dataset

    算法 LOE BRISQUE NIQE 处理时间/s
    RetinexNet[5] 702.80 31.47 3.663 0.0270
    DRBN[21] 462.32 28.69 3.250 0.0241
    Zero-DCE++[22] 433.86 15.37 3.357 0.0012
    EnlightenGAN[12] 408.01 17.63 3.247 0.0150
    KinD++[7] 609.83 23.05 3.813 0.0220
    URetinex-Net[23] 151.21 31.50 3.444 0.0281
    SCI[24] 222.67 37.73 3.568 0.0067
    本文算法 213.78 19.45 3.205 0.0232
    下载: 导出CSV

    表  3  不同算法在公开数据集上的NIQE值

    Table  3.   NIQE of different methods on public datasets

    算法 LIME NPE MEF DICM
    RetinexNet[5] 4.907 4.080 4.904 4.674
    DRBN[21] 4.133 4.112 3.356 3.431
    Zero-DCE++[22] 3.762 4.253 3.283 3.362
    EnlightenGAN[12] 3.608 3.425 3.121 2.846
    KinD++[7] 4.206 3.551 3.374 2.965
    URetinex-Net[23] 4.096 4.041 3.318 3.235
    SCI[24] 4.138 4.160 3.433 3.709
    本文算法 3.557 3.363 3.065 2.622
    下载: 导出CSV

    表  4  消融实验客观评价指标

    Table  4.   Objective evaluation indicators for ablation experiments

    算法 PSNR/dB SSIM
    无颜色一致性 20.138 0.769
    加入BM3D 19.893 0.778
    本文算法 21.221 0.791
    下载: 导出CSV

    表  5  图像增强前后算法检测结果平均精度对比

    Table  5.   Comparison of AP values of algorithm detection results before and after image enhancement

    算法 主干网络 Person Bicycle Car Boat
    增强前 增强后 增强前 增强后 增强前 增强后 增强前 增强后
    SSD300 VGG-16 0.355 0.361 0.344 0.343 0.351 0.346 0.364 0.375
    Deformable DETR ResNet-50 0.382 0.393 0.365 0.375 0.358 0.360 0.381 0.399
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-10
  • 录用日期:  2023-05-19
  • 网络出版日期:  2023-06-27
  • 整期出版日期:  2025-04-30

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