Other studies focus on enhanced image processing methods that extract the desired information from a thermographic image series. [PERSON] et al. [15] applied a Fourier analysis by means of a discrete Fourier transform for each pixel over the image series and selected a certain frequency range to evaluate the mean amplitude of the temporal fluctuations. The evaluation of temporal fluctuations around the mean temperature is unaffected by systematic spatial inhomogeneities within the flow regimes and therefore has the potential to increase the distinguishability between the flow regimes. However, in order to maximize the distinguishability, a priori knowledge about the frequency range of the characteristic temperature fluctuations is needed. Another evaluation of temperature fluctuations without the assumption of a harmonic basis was recently tested by means of a Non-Negative Matrix Factorization [16]. The algorithm evaluates temporal and spatial image information in order to separate superimposed influences on the thermographic image series, leading to a flow visualization result with decreased random and systematic inhomogeneities and, thus, an increased distinguishability between the flow regimes. However, the non-reproducibility of the output images necessitates a manual post-processing as well as a priori knowledge about the approximate location of the flow regimes in order to identify the optimal output image. An enhanced image processing method that also evaluates spatial and temporal image information, without the assumption of a harmonic base, but provides reproducible results, is the principal component analysis (PCA). PCA is already a standard method for thermographic structure analysis [17], but its potential for the thermographic flow analysis has not yet been studied. Furthermore, the combination of classical and enhanced image processing methods seems promising to maximize the flow regimes' distinguishability, which is a pending research task. An important subsequent measurement task based on the flow visualization result is the localization of the laminar-turbulent flow transition. In order to achieve this, different image processing methods have been proposed. The most frequently applied approach is to use either unprocessed raw images or the output of a simple image filtering such as averaging, and then to locate the maximum temperature gradient along the temperature profiles in the main flow direction [12; 18; 19; 20]. For a dynamically changing boundary layer flow, [PERSON] et al. [21] used the computation of differential images to visualize the laminar-turbulent flow transition on a fast pitching airfoil. [PERSON] et al. [14] introduced an automatic localization of the laminar-turbulent flow transition by means of a spatial low-pass filter and a subsequent edge detection algorithm. However, the achievable measurement uncertainty for the localization of the laminar-turbulent flow transition was first investigated by [PERSON] et al. [22] in unprocessed thermographic raw images. According to their findings, the uncertainty is inversely proportional to the temperature gradient between the laminar and turbulent flow regime. Additionally, it was shown that the ideal localization method for locating the flow transition with a minimal uncertainty and a sub-pixel accuracy is to apply a least-squares approximation of the temperature profile with a Gaussian error function. As an alternative, the gradient of the temperature profile can be approximated by a Gaussian function. If the temperature profile or its gradient has the expected course according to the respective approximation, the flow transition position can be extracted directly by the parameters of the fitted approximation function. However, since the ideal temperature course is disturbed by different naturally occurring systematic and random influences, enhanced image processing that reduces these influences has the potential to improve the flow transition localization.