Fig. 11

Analyzes of the row variance \(\sigma _{row,j}^{2}\) of gain normalized images under post-process binning of neighboring pixels. The lines represent the reconstruction using the Pearson correlation coefficients of Fig. 9 under binning. This is achieved following Eq. 26, which yields the variance of the binned image, as well as by utilizing Eqs. 27 and 22, which allow to calculate effect on the measured variance by the correlation of the binned image. The dots represent the results of the regression analyzes similar to Fig. 8. The colors indicate the respective detector quadrant and the colored shades depict the \(95\%\) confidence interval around the measured values. In (a), the row variance \(\sigma _{row,j}\) of a horizontal binning process is shown in dependence of the binning value H. In (b), the vertical binning in dependence of the binning variable V and in (c), the diagonal binning is shown, where rows and columns were HV were increased equally. The Pearson reconstructions generally are in good agreement with the regression. However, the regression analysis of Q1 leading to the red dots, shows a slight deviation to higher values.