A Complementary Color Wavelet-based Measure on Color Image Sharpness Assessment for Autofocus
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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.Abstract: In this paper, a new measure on color image sharpness assessment for autofocus is proposed. By means of the complementary color wavelet transform (CCWT), the scale maximum energy and statistical distribution variabilities of the fusion complementary color operators in CCWT domain are combined into an image sharpness assessment measure by their product. The analyses show that the complementary color operators can extract the characteristics of the color, brightness, scales, directions and mutual information among the channels in a color image. In this way, the sharpness of the most significant feature in these extracted characteritics is revealed by the scale maximum energy of the complementary color operators. The dispersion degree of the features related to the sharpness of an image is disclosed by the distribution variabilities of the operators. As a result, the proposed measure in this paper can increase with the increase of the image sharpness. The simulation results on the LIVE/IVC database show that the proposed measure is of the highest focusing accuracy 0.0373/0.0246 and resolution 1.6132/0.4771, and the best unbiased stability comparison with the classical methods reported in literature.
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Key words:
- Sharpness assessment /
- complementary color wavelet /
- color image /
- maximum energy /
- variability
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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 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 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 1 0.28 3 13 0.964 7 2 0.29 3 14 1 7 3 0.3 3 15 1.1 7 4 0.32 3 16 1.2 9 5 0.33 3 17 1.33 9 6 0.34 3 18 1.67 11 7 0.35 3 19 2 13 8 0.4 3 20 2.33 15 9 0.5 3 21 2.67 17 10 0.66 5 22 3 19 11 0.8 5 23 3.33 21 12 0.906 7 24 3.67 23 
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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 $\Delta H/H$ 1 0.0254 13 0.0278 2 0.0253 14 0.0281 3 0.0252 15 0.0290 4 0.0246 16 0.0303 5 0.0243 17 0.0225 6 0.0239 18 0.0367 7 0.0235 19 0.0415 8 0.0224 20 0.0681 9 0.0241 21 0.1853 10 0.0259 22 0.4329 11 0.0265 23 0.8159 12 0.0274 24 1.2271
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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] 5.4621 99.32 9.0081 82.09 Tenengrad[6] 0.1130 66.99 6.1262 73.67 SMD[8] 0.0756 50.66 6.1054 73.58 SML[9] 0.0543 31.31 3.2748 50.74 Variance[15] 0.5798 93.57 8.6133 81.27 Sd-Svd[11] 0.0380 1.84 2.1322 24.34 DB[15] 0.0950 0.74 6.5152 75.24 FISH[14] 0.0636 41.35 3.9004 58.64 MDB 0.0407 8.35 2.4862 35.11 MCCWT 0.0373 − 1.6132 −
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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] 2.5343 99.03 9.1027 94.47 Tenengrad[6] 0.0785 68.66 6.2105 92.32 SMD[8] 0.0517 52.42 6.0649 92.13 SML[9] 0.0323 23.84 1.7217 72.29 Variance[15] 0.4067 93.95 8.7291 94.53 Sd-Svd[11] 0.0265 7.17 2.2631 78.92 DB[15] 0.0645 61.86 6.4927 92.65 FISH[14] 0.0404 39.11 2.8601 83.32 MDB 0.0299 17.73 1.6301 70.73 MCCWT 0.0246 − 0.4771 −
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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)
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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)
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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)
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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
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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
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