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评价彩色图像自动聚焦清晰度的互补色小波测度

周汶 李旦 张建秋

周汶, 李旦, 张建秋. 评价彩色图像自动聚焦清晰度的互补色小波测度. 自动化学报, 2020, 46(8): 1615−1627 doi: 10.16383/j.aas.c190513
引用本文: 周汶, 李旦, 张建秋. 评价彩色图像自动聚焦清晰度的互补色小波测度. 自动化学报, 2020, 46(8): 1615−1627 doi: 10.16383/j.aas.c190513
Zhou Wen, Li Dan, Zhang Jian-Qiu. A complementary color wavelet-based measure on color image sharpness assessment for autofocus. Acta Automatica Sinica, 2020, 46(8): 1615−1627 doi: 10.16383/j.aas.c190513
Citation: Zhou Wen, Li Dan, Zhang Jian-Qiu. A complementary color wavelet-based measure on color image sharpness assessment for autofocus. Acta Automatica Sinica, 2020, 46(8): 1615−1627 doi: 10.16383/j.aas.c190513

评价彩色图像自动聚焦清晰度的互补色小波测度

doi: 10.16383/j.aas.c190513
基金项目: 国家自然科学基金(11827808, 11974082), 上海市科技创新行动计划社会发展科技领域项目(19DZ1205805), 上海航天科技创新基金, 珠海复旦创新研究院项目资助
详细信息
    作者简介:

    周汶:复旦大学电子工程系硕士研究生. 主要研究方向为计算机视觉, 图像处理.E-mail: wenzhou17@fudan.edu.cn

    李旦:复旦大学电子工程系副教授. 主要研究方向为数字信号处理及应用. 本文通信作者.E-mail: lidan@fudan.edu.cn

    张建秋:复旦大学电子工程系教授. 主要研究方向为信号处理及其在通信、控制、测量、图像和雷达中的应用. E-mail: jqzhang@ieee.org

A Complementary Color Wavelet-based Measure on Color Image Sharpness Assessment for Autofocus

Funds: Supported by National Natural Science Foundation of China (11827808, 11974082), Social Development Project of Shanghai Science and Technology Innovation Action Plan (19DZ1205805), Shanghai Aerospace Science and Technology innovation Fund, Zhuhai-Fudan Innovation Research Institute Project
  • 摘要: 针对彩色图像的自动聚焦, 本文提出了一种新的清晰度评价测度. 该测度借助于互补色小波变换, 在互补色小波域, 利用融合互补色算子的层级最大能量和层级统计分布扩散度的乘积来描述本文的清晰度. 分析表明: 融合互补色算子, 可提取待评价彩色图像在颜色、亮度、方向、尺度和各通道分量间的相互信息等方面的相关特征. 这样, 其层级的最大能量就反映了这些被提取相关特征中最显著特征的清晰程度, 而其统计分布的扩散度, 就衡量了其清晰度特征分布的离散程度. 那么利用它们来共同表征清晰度的测度, 就使得本文所提的测度能随图像清晰程度的增加而增加. 在LIVE/IVC数据库上与多种经典方法的对比结果表明: 本文提出的测度具有最高的聚焦精度0.0373/0.0246、分辨率1.6132/0.4771和最好的无偏稳定性.
  • 图  1  HSI颜色空间与RGB色环

    Fig.  1  The HSI color space and the RGB hue ring

    图  6  层级1的方向子带的统计分布情况

    Fig.  6  Statistical distribution of directional subbands in level 1

    图  2  亮度值恒定的彩色图像

    Fig.  2  A color image with a constant intensity/brightness

    图  3  彩色图像在第2层级的(互补色)小波处理结果

    Fig.  3  Color image processing in level 2 using (Complementary color) wavelet transform

    图  4  MCCWT测度构建流程图($j = 1,2$表示互补色小波分解的层级; $n = k\pi /8,k = 1,2,\cdots,8$表示每一层级的8个子带的方向)

    Fig.  4  Modeling flow of MCCWT measure ($j = 1,2$ presents the decomposition level of the complementary color wavelet; $n = k\pi /8,k = 1,2,\cdots,8$ represents the subbands directions in each level)

    图  5  层级最大能量随模糊递增的变化趋势(实线为层级1, 虚线为层级2)

    Fig.  5  The change trend of the scale maximum energy with the increasing blur of color image (Solid line is level 1, dotted line is level 2)

    图  7  Woman hat图像1和图像3在第1层级、方向为$n = \pi /8$的扩散系数集合统计的真实分布及拟合分布(实线为真实分布, 虚线为拉普拉斯拟合分布)

    Fig.  7  Empirical and fitting distribution of diffusion coefficients of woman hat image 1 and image 3 with direction $n = \pi /8$ in level 1 (The solid line is the empirical distribution, and the dashed line is the Laplace fitting distribution)

    图  8  DIV2K图像序列层级分布的扩散度随模糊递增的变化趋势(实线为层级1, 虚线为层级2)

    Fig.  8  The change trend of the variability with the increasing blur of DIV2K image sequences (Solid line is level 1, dotted line is level 2)

    图  9  聚焦精度的定义

    Fig.  9  Definition of accuracy metrics

    图  10  MDB测度构建流程图($j = 1,2$表示小波分解层级; $n = k\pi /4,k = 1,2,3,4$表示每个层级子带的方向)

    Fig.  10  Modeling flow of MDB measure ($j = 1,2$ represents the decomposition level of the traditional wavelet; $n = k\pi /4,k = 1,2,3,4$ represents the subbands directions in each level)

    图  11  各清晰度评价方法在图像序列上的性能定性比较

    Fig.  11  Qualitative comparison of the performance of each sharpness assessment method in the Gaussian blur image sequences

    表  1  图像高斯模糊的方差及窗口大小

    Table  1  The variance and window size of image Gaussian blur

    图像$\sigma _h^2 $窗口大小图像$\sigma _h^2 $窗口大小
    10.283130.9647
    20.2931417
    30.33151.17
    40.323161.29
    50.333171.339
    60.343181.6711
    70.35319213
    80.43202.3315
    90.53212.6717
    100.66522319
    110.85233.3321
    120.9067243.6723
    下载: 导出CSV

    表  2  DIV2K中层级1、方向为$n = 4\pi /8$的子带的真实分布与拟合分布熵差

    Table  2  In DIV2K, the entropy difference between the empirical distribution and the fitting distribution of the subband with direction $n = 4\pi /8$ in level 1

    图像$\Delta H/H$图像$\Delta H/H$
    10.0254130.0278
    20.0253140.0281
    30.0252150.0290
    40.0246160.0303
    50.0243170.0225
    60.0239180.0367
    70.0235190.0415
    80.0224200.0681
    90.0241210.1853
    100.0259220.4329
    110.0265230.8159
    120.0274241.2271
    下载: 导出CSV

    表  3  各清晰度评价算法在LIVE数据库gblur图像序列中的聚焦精度和分辨率均值(e = 1%)

    Table  3  The average of accuracy metrics and resolution metrics of each sharpness assessment method in Gaussian blur image sequences of LIVE (gblur) database (e = 1%)

    Methods精度(AM)精度提升(%)分辨率(RM)分辨率提升(%)
    Entropy[3]5.462199.329.008182.09
    Tenengrad[6]0.113066.996.126273.67
    SMD[8]0.075650.666.105473.58
    SML[9]0.054331.313.274850.74
    Variance[15]0.579893.578.613381.27
    Sd-Svd[11]0.03801.842.132224.34
    DB[15]0.09500.746.515275.24
    FISH[14]0.063641.353.900458.64
    MDB0.04078.352.486235.11
    MCCWT0.03731.6132
    下载: 导出CSV

    表  4  各清晰度评价算法在IVC数据库Flou图像序列中的聚焦精度和分辨率均值(e = 1%)

    Table  4  The average of accuracy metrics and resolution metrics of each sharpness assessment method in Gaussian blur image sequences of IVC (Flou) database (e = 1%)

    Methods精度(AM)精度提升(%)分辨率(RM)分辨率提升(%)
    Entropy[3]2.534399.039.102794.47
    Tenengrad[6]0.078568.666.210592.32
    SMD[8]0.051752.426.064992.13
    SML[9]0.032323.841.721772.29
    Variance[15]0.406793.958.729194.53
    Sd-Svd[11]0.02657.172.263178.92
    DB[15]0.064561.866.492792.65
    FISH[14]0.040439.112.860183.32
    MDB0.029917.731.630170.73
    MCCWT0.02460.4771
    下载: 导出CSV

    表  5  各清晰度评价算法在LIVE数据库gblur图像序列中的最大值错误次数(噪声存在于图像2)

    Table  5  The number of errors in the maximum value of each sharpness assessment method in the Gaussian blur image sequence of LIVE (gblur) database (noise in image 2)

    Methods$ \sigma _n^2 = 5 $$ \sigma _n^2 = 10 $$ \sigma _n^2 = 15 $$ \sigma _n^2 = 20 $
    Entropy[3]13222427
    Tenengrad[6]072024
    SMD[8]8232728
    SML[9]8222628
    Variance[15]00613
    Sd-Svd[11]8232728
    DB[15]6202528
    FISH[14]2202528
    MDB0009
    MCCWT0000
    下载: 导出CSV

    表  6  各清晰度评价算法在LIVE数据库gblur图像序列中的最大值错误次数(噪声存在于图像3)

    Table  6  The number of errors in the maximum value of each sharpness assessment method in the Gaussian blur image sequence of LIVE (gblur) database (noise in image 3)

    Methods$ \sigma _n^2 = 5 $$ \sigma _n^2 = 10 $$ \sigma _n^2 = 15 $$ \sigma _n^2 = 20 $
    Entropy[3]13242627
    Tenengrad[6]0122125
    SMD[8]11232828
    SML[9]8232628
    Variance[15]01813
    Sd-Svd[11]8242828
    DB[15]8222628
    FISH[14]2222628
    MDB00314
    MCCWT0001
    下载: 导出CSV

    表  7  各清晰度评价算法在LIVE数据库gblur图像序列中的最大值错误次数(噪声存在于图像4)

    Table  7  The number of errors in the maximum value of each sharpness assessment method in the Gaussian blur image sequence of LIVE (gblur) database (noise in image 4)

    Methods$ \sigma _n^2 = 5 $$ \sigma _n^2 = 10 $$ \sigma _n^2 = 15 $$ \sigma _n^2 = 20 $
    Entropy[3]17252827
    Tenengrad[6]0132326
    SMD[8]11252828
    SML[9]10232828
    Variance[15]051115
    Sd-Svd[11]8242828
    DB[15]10222728
    FISH[14]6232828
    MDB00921
    MCCWT0002
    下载: 导出CSV

    表  8  各清晰度评价算法在LIVE数据库gblur图像序列中的平均运行时间

    Table  8  Average running time of each sharpness assessment method in LIVE (gblur) database

    MethodsTime (s)MethodsTime (s)
    Entropy[3]2.6349Sd-Svd[11]0.1865
    Tenengrad[6]0.0371DB[15]0.0831
    SMD[8]0.0283FISH[14]0.0806
    SML[9]0.0234MDB0.1417
    Variance[15]0.0084MCCWT0.7736
    下载: 导出CSV

    表  9  各清晰度评价算法在IVC数据库Flou图像序列中的平均运行时间

    Table  9  Average running time of each sharpness assessment method in IVC (Flou) database

    MethodsTime (s)MethodsTime (s)
    Entropy[3]2.1115Sd-Svd[11]0.1712
    Tenengrad[6]0.0294DB[15]0.0718
    SMD[8]0.0222FISH[14]0.0658
    SML[9]0.0203MDB0.1138
    Variance[15]0.0063MCCWT0.6111
    下载: 导出CSV
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  • 收稿日期:  2019-07-04
  • 录用日期:  2020-04-16
  • 网络出版日期:  2020-08-26
  • 刊出日期:  2020-08-26

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