Non-uniform illumination speckle image correction based on multi-scale bilateral filtering Retinex
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摘要:
为了解决场景中非均匀光照导致散斑图像质量下降，进而影响数字图像相关测试精度的问题，采用多尺度双边滤波Retinex算法对散斑图像进行光学校正，实现光照不均匀场景下位移场与应力场的高精度光学测量；进行了模拟散斑图像的亚像素位移的实验，并与现有图像光学校正算法进行对比，取得了相应散斑图像位移均值误差、标准偏差以及质量评价指标数据。结果表明，所提出的算法对于非均匀光照散斑图像具有良好的校正效果，可以解决图像边界模糊和局部过度校正的问题，校正后散斑图像的平均灰度梯度从12.986上升到14.574，位移标准偏差从0.0443下降到0.0335。该方法可以增强散斑质量，保证光照不均匀场景下数字图像相关方法的测试精度和稳定性，提高复杂光照场景下数字图像相关方法的测试精度，为实际环境中光照突变的非接触测量提供了参考。
Abstract:In order to solve the problem of speckle image quality degradation caused by non-uniform lighting in the scene, which affects the testing accuracy of digital image correlation, the multi-scale bilateral filtering Retinex algorithm was used to perform optical correction on speckle images, achieving high-precision optical measurement of displacement and stress fields in non-uniform lighting scenes. Experiments were conducted to simulate sub-pixel displacement of speckle images and compared them with existing image optical correction algorithms, the corresponding speckle image displacement mean error, standard deviation, and quality evaluation index data were respectively obtained. The results show that the proposed algorithm has a good correction effect on non-uniform illumination speckle images, and the problems of image boundary blur and local overcorrection can be solved. The average grayscale gradient of the corrected speckle image increased from 12.986 to 14.574, and the displacement standard deviation decreased from 0.0443 to 0.0335. This method can effectively enhance the quality of speckle, ensure the testing accuracy and stability of digital image correlation methods in scenarios with uneven lighting, improve the testing accuracy of digital image correlation methods in complex lighting scenarios, and provide reference for non-contact measurement of lighting mutations in actual environments.
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图 1 多尺度双边滤波Retinex算法流程图
Figure 1. Flow chart of the multi-scale bilateral filtering Retinex algorithm
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图 5 4幅不同光照模拟散斑图像的亚像素位移实验结果
Figure 5. Subpixel displacement experiment results of four different illumination simulated speckle images
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图 8 3幅模拟散斑校正后亚像素位移实验结果
Figure 8. Subpixel displacement experiment results after correction of three simulation speckle images
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图 9 非均匀光照散斑图像校正算法对比
Figure 9. Comparison of algorithms for non-uniform illumination speckle image correction
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图 10 校正后散斑图的位移场分布
Figure 10. Displacement field distribution of corrected speckle images
表 1 4幅散斑图像平均均值误差与平均标准偏差
Table 1 Average mean error and average standard deviation of four speckle images
speckle image simulated speckle sample Ⅰ sample Ⅱ sample Ⅲ |ue|/pixel 0.00036 0.00079 0.00104 0.00132 σe/pixel 0.00100 0.01771 0.02234 0.04013 
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表 2 双边滤波参数
Table 2 Parameters of the bilateral filter
parameter sample Ⅰ sample Ⅱ sample Ⅲ σr/pixel (20，80，240) (20，80，250) (20，80，250) σv/pixel (1，2，3) (1，1.5，2) (1，1.5，2)
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表 3 校正前后3幅散斑图像均值误差与标准偏差
Table 3 Mean error and standard deviation of three speckle images before and after correction
parameter sample Ⅰ sample Ⅱ sample Ⅲ before correction after correction before correction after correction before correction after correction |ue|/pixel 0.00079 0.00073 0.00104 0.00077 0.00132 0.00086 σe/pixel 0.01771 0.01021 0.02234 0.01156 0.04013 0.01387 MIG 10.5043 19.9006 13.2436 20.9006 7.84471 20.6419
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表 4 各算法校正后散斑图像的评价指标
Table 4 Evaluation metrics for speckle images after correction by different algorithms
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表 5 非均匀光照散斑图像校正前后评价指标
Table 5 Evaluation indicators for non-uniform illumination speckle images before and after correction
speckle image normal light speckle images before displacement after displacement sample Ⅳ sample Ⅴ sample Ⅳ sample Ⅴ σe/pixel — 0.0443 0.0335 MIG 17.759 12.976 12.995 14.579 14.569 PSNR — 14.756 14.827 19.064 19.295 SSIM — 0.1311 0.1587 0.2348 0.2819
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