Abstract Within the context of Digital Image Correlation (DIC), the optimal treatment of color images is considered. The mathematical bses of a weighted 3-field image correlation are first introduced, which are relevant for RGB encoded images. In this framework, noise characterization methods are developed as noise properties dictate the best suited metric to compare images. Consistent ways to process an image from elementary Bayer matrices are derived. Last, a case study on uncertainty quantification is performed.

中文翻译：

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最佳数字彩色图像相关性

摘要 在数字图像相关 (DIC) 的背景下，考虑了对彩色图像的最佳处理。首先介绍加权 3 场图像相关性的数学基础，它们与 RGB 编码图像相关。在此框架中，由于噪声特性决定了最适合比较图像的指标，因此开发了噪声表征方法。导出了从基本拜耳矩阵处理图像的一致方法。最后，进行了不确定性量化的案例研究。