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一种多图像局部结构化融合的低照度图像增强算法

徐少平 张贵珍 林珍玉 刘婷云 李崇禧

徐少平, 张贵珍, 林珍玉, 刘婷云, 李崇禧. 一种多图像局部结构化融合的低照度图像增强算法. 自动化学报, 2022, 48(10): 1−15 doi: 10.16383/j.aas.c190417
引用本文: 徐少平, 张贵珍, 林珍玉, 刘婷云, 李崇禧. 一种多图像局部结构化融合的低照度图像增强算法. 自动化学报, 2022, 48(10): 1−15 doi: 10.16383/j.aas.c190417
Xu Shao-Ping, Zhang Gui-Zhen, Lin Zhen-Yu, Liu Ting-Yun, Li Chong-Xi. A multi-image local structured fusion-based low-light image enhancement algorithm. Acta Automatica Sinica, 2022, 48(10): 1−15 doi: 10.16383/j.aas.c190417
Citation: Xu Shao-Ping, Zhang Gui-Zhen, Lin Zhen-Yu, Liu Ting-Yun, Li Chong-Xi. A multi-image local structured fusion-based low-light image enhancement algorithm. Acta Automatica Sinica, 2022, 48(10): 1−15 doi: 10.16383/j.aas.c190417

一种多图像局部结构化融合的低照度图像增强算法

doi: 10.16383/j.aas.c190417
基金项目: 国家自然科学基金(61662044, 62162043, 51765042), 江西省自然科学基金(20171BAB202017)资助
详细信息
    作者简介:

    徐少平:南昌大学数学与计算机学院教授. 主要研究方向为数字图像处理与分析, 计算机图形学, 虚拟现实, 手术仿真. 本文通信作者. E-mail: xushaoping@ncu.edu.cn

    张贵珍:南昌大学数学与计算机学院硕士研究生. 主要研究方向为图像处理与计算机视觉. E-mail: 406130917331@email.ncu.edu.cn

    林珍玉:南昌大学数学与计算机学院硕士研究生. 主要研究方向为图像处理与计算机视觉. E-mail: 401030918076@email.ncu.edu.cn

    刘婷云:南昌大学数学与计算机学院硕士研究生. 主要研究方向为图像处理与计算机视觉. E-mail: 416114517210@email.ncu.edu.cn

    李崇禧:南昌大学数学与计算机学院硕士研究生. 主要研究方向为图像处理与计算机视觉. E-mail: 406130917315@email.ncu.edu.cn

A Multi-image Local Structured Fusion-based Low-light Image Enhancement Algorithm

Funds: Supported by National Natural Science Foundation of China (61662044, 62162043, 51765042) and Natural Science Foundation of Jiangxi Province (20171BAB202017)
More Information
    Author Bio:

    XU Shao-Ping Professor at the School of Mathematics and Computer Sciences, Nanchang University. His research interest covers digital image processing and analysis, computer graphics, virtual reality, and surgery simulation. Corresponding author of this paper

    ZHANG Gui-Zhen Master student at the School of Mathematics and Computer Sciences, Nanchang University. Her research interest covers image processing and computer vision

    LIN Zhen-Yu Master student at the School of Mathematics and Computer Sciences, Nanchang University. Her research interest covers image processing and computer vision

    LIU Ting-Yun Master student at the School of Mathematics and Computer Sciences, Nanchang University. Her research interest covers image processing and computer vision

    LI Chong-Xi Master student at the School of Mathematics and Computer Sciences, Nanchang University. His research interest covers image processing and computer vision

  • 摘要: 为将低照度图像及基于它生成的多个不同曝光度图像中的互补性信息进行最佳融合以获得更为鲁棒的视觉增强效果, 提出了一种基于多图像局部结构化融合的两阶段低照度图像增强(Low-light image enhancement, LLIE)算法. 在待融合图像制备阶段, 提出了一种基于图像质量评价的最佳曝光度预测模型, 利用该预测模型给出的关于低照度图像最佳曝光度值, 在伪曝光模型下生成适度增强图像和过曝光图像 (利用比最佳曝光度值更高的曝光度生成)各一幅. 同时, 利用经典Retinex模型生成一幅适度增强图像作为补充图像参与融合. 在融合阶段, 首先将低照度图像、适度增强图像(2幅)和过曝光图像在同一空间位置处的图块矢量化后分解为对比度、结构强度和亮度三个分量. 之后, 以所有待融合对比度分量中的最高值作为融合后的对比度分量值, 而结构强度和亮度分量则分别以相位一致性映射图和视觉显著度映射图作为加权系数完成加权融合. 然后, 将分别融合后的对比度、纹理结构和亮度三个分量重构为图块, 并重新置回融合后图像中的相应位置. 最后, 在噪声水平评估算法导引下自适应调用降噪算法完成后处理. 实验结果表明: 所提出的低照度图像增强算法在主客观图像质量评价上优于现有大多数主流算法.
  • 图  1  Ying算法中实现低照度图像增强的融合框架

    Fig.  1  Fusion framework of low-light image enhancement in Ying algorithm

    图  2  待融合图像纹理结构权重的比较分析

    Fig.  2  The weight analysis of the texture structure component for images to be fused

    图  3  待融合图像亮度值权重的比较分析

    Fig.  3  The weight analysis of the brightness component for images to be fused

    图  4  测试集中选出的8幅有代表性的图像集合

    Fig.  4  Eight representative images selected from the test set

    图  5  各个算法在Tree图上增强效果对比

    Fig.  5  Visual comparison of the results obtained with competing algorithms on Tree image

    图  7  各个算法在Class图上增强效果对比

    Fig.  7  Visual comparison of the results obtained with competing algorithms on Class image

    图  6  各个算法在Tower图上增强效果对比

    Fig.  6  Visual comparison of the results obtained with competing algorithms on Tower image

    图  8  各个算法在Community图像上抑制噪声效果对比

    Fig.  8  Comparison of denoising effect of each algorithm on Community image

    表  1  各个对比算法对Tree图像增强后的无参考指标值比较

    Table  1  Performance comparison of each algorithm on Tree image

    算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE
    BOIEM 1.96 2.07 2.18 1.97 2.11 2.26 2.25 2.37
    BIQME 0.28 0.30 0.37 0.35 0.48 0.37 0.43 0.39
    NIQMC 3.31 3.66 4.18 3.27 4.69 3.54 3.40 4.17
    IL-NIQE 57.66 60.04 57.39 58.69 58.08 49.92 65.06 31.79
    下载: 导出CSV

    表  2  各个对比算法对图像Tower增强后的无参考指标值

    Table  2  Performance comparison of each algorithm on Tower image

    算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE
    BOIEM 2.30 3.42 3.04 2.90 3.18 2.77 3.42 3.15
    BIQME 0.40 0.54 0.48 0.53 0.54 0.53 0.58 0.56
    NIQMC 3.48 4.97 4.58 4.63 4.63 4.22 4.92 4.70
    IL-NIQE 42.82 32.34 37.05 34.68 35.04 38.66 36.75 30.10
    下载: 导出CSV

    表  3  各个对比算法对图像Class增强后的无参考指标值

    Table  3  Performance comparison of each algorithm on Class image

    算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE
    BOIEM 2.67 2.68 3.42 2.64 3.38 2.61 2.63 3.17
    BIQME 0.48 0.50 0.57 0.53 0.6 0.55 0.53 0.56
    NIQMC 4.57 4.57 5.28 4.67 5.00 4.67 4.33 4.95
    IL-NIQE 25.89 22.22 20.9 24.26 22.99 38.22 24.47 20.97
    IW-PNSR 16.68 17.36 21.36 18.66 15.24 16.46 17.93 22.18
    IW-SSIM 0.85 0.87 0.94 0.91 0.87 0.83 0.88 0.95
    下载: 导出CSV

    表  4  各个算法在90幅无参考低照度图像上的无参考指标的平均值

    Table  4  Average performance of different competing algorithms on 90 low-light images without reference images

    算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE
    BOIEM 3.07 3.17 3.27 3.13 3.38 3.12 3.03 3.21
    BIQME 0.54 0.56 0.57 0.57 0.61 0.58 0.58 0.58
    NIQMC 4.73 4.88 5.02 4.81 5.33 4.8 4.64 4.89
    IL-NIQE 28.69 27.18 27.15 27.61 28.21 30.94 28.62 25.28
    下载: 导出CSV

    表  5  各个算法在90幅有参考低照度图像上的无参考和有参考指标的平均值

    Table  5  Average performance of different competing algorithms on 90 low-light images with reference images

    算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE
    BOIEM 2.67 2.68 3.42 2.64 3.38 2.61 2.63 3.17
    BOIEM 3.38 3.41 3.43 3.34 3.43 3.38 3.28 3.40
    BIQME 0.58 0.60 0.58 0.58 0.61 0.58 0.59 0.59
    NIQMC 5.11 5.15 5.10 4.97 5.34 5.06 4.84 5.02
    IL-NIQE 23.14 22.74 22.42 22.29 24.69 25.54 22.62 22.19
    IW-PSNR 20.35 19.81 22.10 22.15 15.31 22.04 21.88 22.54
    IW-SSIM 0.92 0.91 0.94 0.94 0.84 0.93 0.94 0.95
    下载: 导出CSV

    表  6  各个算法在90幅有参考低照度图像上的平均执行时间比较(s)

    Table  6  The average execution time of the competing algorithms on 90 low-light images with reference images (s)

    算法 HVS Fu2016a Fu2016b Ying LIME Li FFMD MLSF-LLIE
    时间 0.56 0.29 1.93 0.21 0.10 11.23 4.13 4.21
    下载: 导出CSV
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  • 收稿日期:  2019-05-29
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