Digital image correlation with gray gradient constraints: Application to spatially variant speckle images

As a carrier of local deformation information, speckle pattern inside a subset is usually crucial for surface displacement acquisition based upon a digital image correlation (DIC) method, since both accuracy and precision of DIC method are closely related to the amount of speckle information in a subset. Although some comprehensive theoretical frameworks have been developed to estimate the quality of local speckle patterns, it is still a great challenge how to effectively integrate the subset speckle information into the well-developed correlation criteria used for DIC. By means of a well-designed square window function, we here propose the concept of continuous subset in order to modulate subset size in a continuously derivable manner. Afterwards, we further develop a new constrained zero-normalized sum-of-squared differences (CZNSSD) criterion and construct the corresponding iterative algorithm, based on which the subset size involved can be automatically determined according to the necessary amount of speckle information. Numerical results of synthetic speckle images indicate that the set of algorithm can enhance the accuracy and precision of displacement measurement, especially for spatially variant speckle images.     

Quality assessment of speckle patterns for digital image correlation

Digital image correlation (DIC) is an optical–numerical full-field displacement measuring technique, which is nowadays widely used in the domain of experimental mechanics. The technique is based on a comparison between pictures taken during loading of an object. For an optimal use of the method, the object of interest has to be covered with painted speckles. In the present paper, a comparison is made between three different speckle patterns originated by the same reference speckle pattern. A method is presented for the determination of the speckle size distribution of the speckle patterns, using image morphology. The images of the speckle patterns are numerically deformed based on a finite element simulation. Subsequently, the displacements are measured with DIC-software and compared to the imposed ones. It is shown that the size of the speckles combined with the size of the used pixel subset clearly influences the accuracy of the measured displacements.     

Comparison of subset-based local and FE-based global digital image correlation: Theoretical error analysis and validation

Subset-based local and finite-element-based (FE-based) global digital image correlation (DIC) approaches are the two primary image matching algorithms widely used for full-field displacement mapping. Very recently, the performances of these different DIC approaches have been experimentally investigated using numerical and real-world experimental tests. The results have shown that in typical cases, where the subset (element) size is no less than a few pixels and the local deformation within a subset (element) can be well approximated by the adopted shape functions, the subset-based local DIC outperforms FE-based global DIC approaches because the former provides slightly smaller root-mean-square errors and offers much higher computation efficiency. Here we investigate the theoretical origin and lay a solid theoretical basis for the previous comparison. We assume that systematic errors due to imperfect intensity interpolation and undermatched shape functions are negligibly small, and perform a theoretical analysis of the random errors or standard deviation (SD) errors in the displacements measured by two local DIC approaches (i.e., a subset-based local DIC and an element-based local DIC) and two FE-based global DIC approaches (i.e., Q4-DIC and Q8-DIC). The equations that govern the random errors in the displacements measured by these local and global DIC approaches are theoretically derived. The correctness of the theoretically predicted SD errors is validated through numerical translation tests under various noise levels. We demonstrate that the SD errors induced by the Q4-element-based local DIC, the global Q4-DIC and the global Q8-DIC are 4, 1.8–2.2 and 1.2–1.6 times greater, respectively, than that associated with the subset-based local DIC, which is consistent with our conclusions from previous work.     

A fast digital image correlation method for deformation measurement

Fast and high-accuracy deformation analysis using digital image correlation (DIC) has been increasingly important and highly demanded in recent years. In literature, the DIC method using the Newton-Rapshon (NR) algorithm has been considered as a gold standard for accurate sub-pixel displacement tracking, as it is insensitive to the relative deformation and rotation of the target subset and thus provides highest sub-pixel registration accuracy and widest applicability. A significant drawback of conventional NR-algorithm-based DIC method, however, is its extremely huge computational expense. In this paper, a fast DIC method is proposed deformation measurement by effectively eliminating the repeating redundant calculations involved in the conventional NR-algorithm-based DIC method. Specifically, a reliability-guided displacement scanning strategy is employed to avoid time-consuming integer-pixel displacement searching for each calculation point, and a pre-computed global interpolation coefficient look-up table is utilized to entirely eliminate repetitive interpolation calculation at sub-pixel locations. With these two approaches, the proposed fast DIC method substantially increases the calculation efficiency of the traditional NR-algorithm-based DIC method. The performance of proposed fast DIC method is carefully tested on real experimental images using various calculation parameters. Results reveal that the computational speed of the present fast DIC is about 120-200 times faster than that of the traditional method, without any loss of its measurement accuracy     

Assessment of speckle-pattern quality in digital image correlation based on gray intensity and speckle morphology

In digital image correlation (DIC), speckle patterns are generated on the surface of a specimen to resolve uniqueness issues. Thus, speckle patterns significantly affect the accuracy of image correlation. To assess the quality of speckle patterns, the standard deviation of gray intensities within each speckle (SDGIS) is introduced as a new metric. On the basis of the cumulative distribution of SDGIS, speckle-pattern quality measurement (ρ) is proposed, which integrates the features of gray intensity and speckle morphology. Twelve speckle patterns are generated by changing the spraying time and nozzle sizes of an airbrush because these are associated with the speckle volume fraction and speckle size, respectively. In addition, three displacement fields are used to investigate the effects of speckle patterns on the accuracy of the DIC results. For the 12 speckle images associated with the three displacement fields, the correlation results demonstrate that the proposed speckle-pattern quality measurement is inversely proportional to the averaged error of the subset method. This is statistically confirmed by evaluating the correlation coefficient and p-value. Furthermore, the error of the subset method is more affected by speckle patterns than the subset size when the subset size is sufficiently large.     

Digital image correlation involves an inverse problem: A regularization scheme based on subset size constraint

Digital image correlation (DIC) is essentially implicated in a class of inverse problem. Here, a regularization scheme is developed for the subset-based DIC technique to effectively inhibit potential ill-posedness that likely arises in actual deformation calculations and hence enhance numerical stability, accuracy and precision of correlation measurement. With the aid of a parameterized two-dimensional Butterworth window, a regularized subpixel registration strategy is established, in which the amount of speckle information introduced to correlation calculations may be weighted through equivalent subset size constraint. The optimal regularization parameter associated with each individual sampling point is determined in a self-adaptive way by numerically investigating the curve of 2-norm condition number of coefficient matrix versus the corresponding equivalent subset size, based on which the regularized solution can eventually be obtained. Numerical results deriving from both synthetic speckle images and actual experimental images demonstrate the feasibility and effectiveness of the set of newly-proposed regularized DIC algorithms.     

高温下数字图像相关散斑最优成像探究

为了测量材料在高温甚至超高温下的力学性能,采用数字图像相关方法,并研究其在高温下的最优成像。采取不同的散斑制作方法,同时加入不同颜色的高温漆,在不同的温度节点,外加不同光源及相应的滤波片,采集并观察图像是否具有良好的对比度。普通的单色光源在800℃以后会逐渐失效,无法获取图像,而紫外光在1 200℃时依然可以获取较好的图像,且直接利用试件本身颜色作为底色效果更佳。采用紫外光照明可以实现DIC在高温环境下的测量。同时利用黑色或者蓝色散斑直接喷涂在试件上有着最佳的对比度,要优于常规的散斑制作方法。     

Strain field denoising for digital image correlation using a regularized cost-function

In digital image correlation (DIC), the widely used forward-additive Newton–Raphson (FA-NR) algorithm and the recently introduced equivalent but more efficient inverse-compositional Gauss–Newton (IC-GN) algorithm are capable of providing both displacements and displacement gradients (strains) for each calculation point. However, the obtained displacement gradients are seriously corrupted by various noises, and for this reason these directly computed strains are usually considered as useless information and therefore discarded. To extract strain distributions more accurately, much research efforts have been dedicated to how to smooth and differentiate the noisy displacement fields using appropriate numerical approaches. In this contribution, contrary to these existing strain estimation approaches, a novel and alternative strain estimation approach, based on denoising the noisy strain fields obtained by FA-NR or IC-GN algorithm using a regularized cost-function, is proposed. The effectiveness and practicality of the proposed strain estimation technique is carefully examined using both computer-simulated images with imposed homogeneous and inhomogeneous deformation, and experimentally obtained images. Experimental results reveal that the strains obtained by the proposed method are comparable to those determined by post-processing of the displacement fields using conventional pointwise least squares strain estimation approach.     

Calibration and correction of lens distortion for two-dimensional digital speckle correlation measurement

Lens distortion practically presents in optical imaging system using in two-dimensional digital speckle correlation measurement (2D-DSCM) system, and gives rise to additional errors in the displacement and strain measurement. Camera calibration procedure is performed to obtain the coefficients of radial distortion and tangential distortion. The corrected displacement fields can be calculated using the distortion coefficient. The influence of distortion on displacement and strain measurement errors in experiment is further discussed. The three-point bending test result shows that the camera lens calibration method can effectively eliminate the effect of lens distortion and improve displacement and strain measurement accuracy.     

Measuring thickness change of transparent plate by electronic speckle pattern interferometry and digital image correlation

The thickness change of transparent plates was measured by electronic speckle pattern interferometry (ESPI) method and digital image correlation (DIC) method. An out-of-plane ESPI system was developed based on the Michelson interferometer, and a new thickness measurement method was designed, which is on the basis of Snell's law of refraction and DIC. The main principles and experimental procedures of these two methods were presented. The thickness change of polymethyl specimens under uniaxial tensile loading were measured by the optical techniques and compared with each other. The results reveal that the data obtained with DIC method achieve better linearity than ESPI.     

A space-time approach in digital image correlation: Movie-DIC

A new method is proposed to estimate arbitrary velocity fields from a time series of images acquired by a single camera. This approach, here focused on a single spatial plus a time dimension, is specialized to the decomposition of the velocity field over rectangular shaped (finite-element) bilinear shape functions. It is therefore assumed that the velocity field is essentially aligned along one direction. The use of a time sequence over which the velocity is assumed to have a smooth temporal change allows one to use elements whose spatial extension is much smaller than in traditional digital image correlation based on successive image pairs. This method is first qualified by using synthetic numerical test cases, and then applied to a dynamic tensile test performed on a tantalum specimen. Improvements with respect to classical digital image correlation techniques are observed in terms of spatial resolution.     

A novel speckle pattern—Adaptive digital image correlation approach with robust strain calculation

Digital image correlation (DIC) has seen widespread acceptance and usage as a non-contact method for the determination of full-field displacements and strains in experimental mechanics. The advances of imaging hardware in the last decades led to high resolution and speed cameras being more affordable than in the past making large amounts of data image available for typical DIC experimental scenarios. The work presented in this paper is aimed at maximizing both the accuracy and speed of DIC methods when employed with such images. A low-level framework for speckle image partitioning which replaces regularly shaped blocks with image-adaptive cells in the displacement calculation is introduced. The Newton-Raphson DIC method is modified to use the image pixels of the cells and to perform adaptive regularization to increase the spatial consistency of the displacements. Furthermore, a novel robust framework for strain calculation based also on the Newton-Raphson algorithm is introduced. The proposed methods are evaluated in five experimental scenarios, out of which four use numerically deformed images and one uses real experimental data. Results indicate that, as the desired strain density increases, significant computational gains can be obtained while maintaining or improving accuracy and rigid-body rotation sensitivity.     

Speckle pattern quality assessment for digital image correlation

To perform digital image correlation (DIC), each image is divided into groups of pixels known as subsets or interrogation cells. Larger interrogation cells allow greater strain precision but reduce the spatial resolution of the data field. As such the spatial resolution and measurement precision of DIC are limited by the resolution of the image. In the paper the relationship between the size and density of speckles within a pattern is presented, identifying that the physical properties of a pattern have a large influence on the measurement precision which can be obtained. These physical properties are often overlooked by pattern assessment criteria which focus on the global image information content. To address this, a robust morphological methodology using edge detection is devised to evaluate the physical properties of different speckle patterns with image resolutions from 23 to 705 pixels/mm. Trends predicted from the pattern property analysis are assessed against simulated deformations identifying how small changes to the application method can result in large changes in measurement precision. An example of the methodology is included to demonstrate that the pattern properties derived from the analysis can be used to indicate pattern quality and hence minimise DIC measurement errors. Experiments are described that were conducted to validate the findings of morphological assessment and the error analysis.     

Gaussian pre-filtering for uncertainty minimization in digital image correlation using numerically-designed speckle patterns

This paper discusses the effect of pre-processing image blurring on the uncertainty of two-dimensional digital image correlation (DIC) measurements for the specific case of numerically-designed speckle patterns having particles with well-defined and consistent shape, size and spacing. Such patterns are more suitable for large measurement surfaces on large-scale specimens than traditional spray-painted random patterns without well-defined particles. The methodology consists of numerical simulations where Gaussian digital filters with varying standard deviation are applied to a reference speckle pattern. To simplify the pattern application process for large areas and increase contrast to reduce measurement uncertainty, the speckle shape, mean size and on-center spacing were selected to be representative of numerically-designed patterns that can be applied on large surfaces through different techniques (e.g., spray-painting through stencils). Such “designer patterns” are characterized by well-defined regions of non-zero frequency content and non-zero peaks, and are fundamentally different from typical spray-painted patterns whose frequency content exhibits near-zero peaks. The effect of blurring filters is examined for constant, linear, quadratic and cubic displacement fields. Maximum strains between ±250 and ±20,000 µε are simulated, thus covering a relevant range for structural materials subjected to service and ultimate stresses. The robustness of the simulation procedure is verified experimentally using a physical speckle pattern subjected to constant displacements. The stability of the relation between standard deviation of the Gaussian filter and measurement uncertainty is assessed for linear displacement fields at varying image noise levels, subset size, and frequency content of the speckle pattern. It is shown that bias error as well as measurement uncertainty are minimized through Gaussian pre-filtering. This finding does not apply to typical spray-painted patterns without well-defined particles, for which image blurring is only beneficial in reducing bias errors.     

基于单幅数字散斑投影及图像相关的离面振动测量

采用单幅数字散斑图投影及高速数字图像采集技术,研究了动态离面位移的测量。采用商用液晶投影仪将计算机产生的模拟散斑图投影到待测动态变形物体表面,由高速数字图像采集设备摄取并保存变形散斑图,采用时间序列数字图像相关软件计算出物体表面各点随时间变化的离面位移。这种方法用于振动分析时不仅可以获得振型分布,而且还可以获得各点的振幅值。与现有的激光频闪照相测振及激光多普勒测振等方法相比,具有光学系统简单,可全场定量测量。悬臂梁振动实验结果证实了该方法的有效性。     

Full-field digital image correlation with Kriging regression

A full-field Digital Image Correlation (DIC) method with integrated Kriging regression is presented in this article. The displacement field is formulated as a best linear unbiased model that includes the correlations between all the locations in the Region of Interest (RoI). A global error factor is employed to extend conventional Kriging interpolation to quantify displacement errors of the control points. An updating strategy for the self-adaptive control grid is developed on the basis of the Mean Squared Error (MSE) determined from the Kriging model. Kriging DIC is shown to outperform several other full-field DIC methods when using open-access experimental data. Numerical examples are used to demonstrate the robustness of Kriging DIC to different choices of initial control points and to speckle pattern variability. Finally Kriging DIC is tested on an experimental example.     

Two-step digital image correlation for micro-region measurement

A method of two-step digital image correlation is well developed with more stable and reliable calculating technology, which consists of a simple searching method and an iterative correlation method. This new method can not only improve the calculating speed and the measuring accuracy, but also simplify the process of the experiment. In order to further increase the sensitivity of the technique, the sub-pixel reconstruction is performed in sub-image by utilizing the higher precision calculation of bicubic spline interpolation value method, and the accuracy of displacement is extended to better than 0.01 pixel; the strain resolution is limited to less than 0.0002 in micro-region. The above method is applied to quantify the micro-deformation of bimaterial sample and coating sample. The experimental results show that the method of two-step digital image correlation is a potential boon to investigations at extremely small-size scales.     

High-efficiency and high-accuracy digital image correlation for three-dimensional measurement

The computational efficiency and measurement accuracy of the digital image correlation (DIC) have become more and more important in recent years. For the three-dimensional DIC (3D-DIC), these issues are much more serious. First, there are two cameras employed which increases the computational amount several times. Second, because of the differences in view angles, the must-do stereo correspondence between the left and right images is equivalently a non-uniform deformation, and cannot be weakened by increasing the sampling frequency of digital cameras. This work mainly focuses on the efficiency and accuracy of 3D-DIC. The inverse compositional Gauss–Newton algorithm (IC-GN²) with the second-order shape function is firstly proposed. Because it contains the second-order displacement gradient terms, the measurement accuracy for the non-uniform deformation thus can be improved significantly, which is typically one order higher than the first-order shape function combined with the IC-GN algorithm (IC-GN¹), and 2 times faster than the second-order shape function combined with the forward additive Gauss–Newton algorithm (FA-GN²). Then, based on the features of the IC-GN¹ and IC-GN² algorithms, a high-efficiency and high-accuracy measurement strategy for 3D-DIC is proposed in the end.