数字图像相关方法中散斑图的质量评价研究

Study of Speckle Pattern Quality Assessment used in Digital Image Correlation

在利用数字图像相关方法测量物体表面变形时,被测物体表面必需覆盖有灰度随机分布的散斑场,该散斑场作为试件表面变形信息的载体随试件一起变形。在实际情况下,不同的散斑场会显示出完全不同的灰度分布特征,并对数字图像相关方法的测量结果有着重要影响。因此如何定量评价散斑图的优劣是数字图像相关方法中一个重要的基本问题,也是该方法的使用者非常关心的问题。基于最近数字图像相关方法基本理论研究的进展,本文提出平均灰度梯度这一新参数用于散斑图质量的评价。为证实该参数的有效性,本文对五幅明显不同的散斑图进行了精确平移,并将数字图像相关方法测量的位移与预加的平移量进行比较,分析了位移测量结果的均值误差和标准差。结果显示位移测量结果的均值误差和标准差均与散斑图的平均灰度梯度有关,一个好的散斑图应该具有较大的平均灰度梯度。

In the use of digital image correlation （DIC） technique for full-field deformation measurement, the tested object surface must be covered with a random gray speckle pattern, which deforms together with the object surface as a carrier of deformation information. However, in practical applications, the speckle p＇atterns on tested surface may show distinctly different intensity distribution characteristics and have an important influence on DIC measurements. How to assess the overall quality of different speckle patterns with a simple yet effective parameter is an interesting but confusing problem, and is also helpful to optimize the technique. In this paper, a novel, simple, easyto-calculate yet effective global parameter, called mean gray intensity gradient, is proposed for quality assessment of the speckle patterns used in DIC. To verify this new concept, five different speckle patterns were numerically translated, and the displacements measured with DIC were compared with the exact ones. The errors are evaluated in terms of mean bias error and standard deviation error. It shows that both mean bias error and standard deviation of the measured displacement are closely related to the mean gray intensity gradient of the speckle pattern used, and a good speckle pattern should be of large mean intensity gradient.