An Improved Digital Image Correlation Calculation in the Case of Substantial Lighting Variation

In the last decade, digital image correlation (DIC) has become the most widely used non-contact technique for measuring full-field motion and deformation in image sequences. As permanent hardware improvements make it possible to readily obtain digital images with higher and higher resolutions at ever-increasing frame rates, the efficiency of image registration methods is becoming increasingly important. Another aspect of this problem is the desire to improve the accuracy of these methods, which in general implies additional computational time. This problem is addressed by applying the inverse compositional Gauss-Newton (IC-GN) algorithm, which is modified to include the parametric sum of squared differences (PSSD) correlation criterion. However, the classical IC-GN algorithm with the sum of squared differences (SSD) correlation criterion cannot be used in cases in which lighting variations are of considerable importance. In this paper, a solution to this disadvantage is presented. The lighting variation is directly included in the IC-GN minimization procedure, which reduces the additional calculation time. Moreover, this IC-GN algorithm does not need to re-evaluate and invert the Hessian matrix. This results in a more computationally efficient algorithm. Experimental results show that the proposed algorithm is insensitive to the linear intensity variation in the target image; consequently, it has a faster convergence rate than the IC-GN algorithm with the zero-mean normalized sum of squared differences (ZNSSD) correlation criterion. As a result, computational time can be reduced by up to 30% in the case of convergence condition ε =0.001. Owing to the above mentioned advantages of the proposed IC-GN algorithm, it could become a new standard for efficient and robust full-field displacement tracking in image sequences with varying lighting intensity.