Image registration under small displacements is the keystone of several image analysis tasks such as optical flow estimation, stereoscopic imaging, or full-field displacement estimation in photomechanics. A popular approach consists in locally modeling the displacement field between two images by a parametric transformation and performing least-squares estimation afterward. This procedure is known as “digital image correlation” in several domains as in photomechanics. The present article is part of this approach. First, the estimated displacement is shown to be impaired by biases related to the interpolation scheme needed to reach subpixel accuracy, the image gradient distribution, as well as the difference between the hypothesized parametric transformation and the true displacement. A quantitative estimation of the difference between the estimated value and the actual one is of importance in application domains such as stereoscopy or photomechanics, which have metrological concerns. Second, we question the extent to which these biases could be eliminated or reduced. We also present numerical assessments of our predictive formula in the context of photomechanics. Software codes are freely available to reproduce our results. Although this paper is focused on a particular application field, namely photomechanics, it is relevant to various scientific areas concerned by image registration.

小位移下的图像配准是一些图像分析任务的重点，例如光流估计，立体成像或光力学中的全视场位移估计。一种流行的方法是通过参数转换对两个图像之间的位移场进行局部建模，然后执行最小二乘估计。该过程在某些领域被称为“数字图像关联”，如光机械学。本文是该方法的一部分。首先，显示出估计的位移受到与达到子像素精度，图像梯度分布以及假设的参数变换和真实位移之间的差异所需的内插方案相关的偏差的影响。估计值与实际值之间的差异的定量估计在诸如计量学或立体力学之类的应用领域中非常重要。其次，我们质疑可以消除或减少这些偏见的程度。我们还介绍了在光力学背景下对我们的预测公式的数值评估。免费提供软件代码以重现我们的结果。尽管本文侧重于特定的应用领域，即光力学，但它与图像配准相关的各个科学领域有关。我们还介绍了在光力学背景下对我们的预测公式的数值评估。免费提供软件代码以重现我们的结果。尽管本文侧重于特定的应用领域，即光力学，但它与图像配准相关的各个科学领域有关。我们还介绍了在光力学背景下对我们的预测公式的数值评估。免费提供软件代码以重现我们的结果。尽管本文侧重于特定的应用领域，即光力学，但它与图像配准相关的各个科学领域有关。