## 3 Results and Discussion
### Comparison of PT and LST through CNR Analysis
Second, any kind of spatial filtering modifies the input signal to some extent and may corrupt images if not selected properly. Hence, even though it is clear from the results presented herein that the CNR can be significantly improved using Gaussian filtering, additional research should be carried out to better assess the impact of filtering on defect contrast enhancement. Figure 6: Pulsed thermography. Impact of advanced signal processing and Gaussian filtering on **single layer porosity** at **shallow depth**: sample S44, front face, defect 1 (insert at \(z=0.287\) mm, thickness = 0.17 mm). Figures 6 and 7 illustrate the impact of advanced signal processing and filtering on noise reduction, i.e., on CNR values, for the case of a shallow defect, which could in fact be easily detected even without filtering or signal processing given that it is very close to the inspected surface (\(z\) = 0.285 mm). Deeper defects are more challenging as demonstrated next. Figures 8 and 9 include PT raw and processed results (the first 8 columns), as well as the best LST results, i.e., images with the highest CNR\({}_{\rm max}\) (the last two columns in Figure 8, and only the last column in Figure 9). Considering exclusively the impact of advanced signal processing applied to PT data (top line, excluding the last column) in Figure 8, it can be noticed that defect 1 of sample S48 would not be detected without processing as the raw result has a rather low defect contrast, i.e., CNR\({}_{\rm max}\) = 1.1. It is only after processing that the porosity area becomes clearly recognizable (roughly CNR\({}_{\rm max}\)\(\geq\) 3.4). On the other hand, considering exclusively the impact of Gaussian filtering (first column in Figure 8), it can be concluded that filtering also improves the CNR although to a lesser extent than signal processing (at least for the kind of filter and variances utilized herein), passing from CNR\({}_{\rm max}\) = 1.1 without filtering, to CNR\({}_{\rm max}\) = 2.5 for a Gaussian filter with a variance \(\sigma^{2}\) = 1, and to CNR\({}_{\rm max}\) = 3.1 for a Gaussian filter with \(\sigma^{2}\) = 3. The striking combined effect of advanced signal processing and image filtering on defect contrast enhancement can be emphasized by comparing the raw result (the lowest CNR, top-left) to the second time derivative result (the highest CNR, bottom-right) improving the CNR\({}_{\rm max}\) from 1.1 to 11.8. In the case of LST (last 2 columns in Figure 8), it is interesting to note that, contrary to PT, porosity can be detected by LST with good contrast without signal processing or filtering, CNR\({}_{\rm max}\) = 4.5 (see Table 2 for reference). This is due to a longer thermal stimulation in LST that allows heat diffusion to reach the defects and increase contrast. Signal processing does not improve CNR as drastically as PT (in some cases even worsens). The best results are obtained with TSR 1 st time derivative (last column). A pattern of vertical lines can be perceived in the LST results. This pattern apparently corresponds to the grid in front of the heating IR lamp. Interestingly, the 1 st time derivative removes this effect. Figure 7: Line scan thermography at 25 mm/s. Impact of advanced signal processing and Gaussian filtering on **single layer porosity** at **shallow depth**: sample S45, front face, defect 1 (insert at depth = 0.287 mm, thickness = 0.19 mm). Figure 9 summarizes the results for defect 3 in sample S48. Even though both Figures 8 and 9 present results for porosity at an intermediate depth, i.e., close to the middle thickness of sample S48, there are significant differences between them. Defect 1 (presented in Figure 8) is a single-layer porosity area located at \(z=1.7\) mm, whilst defect 3 (Figure 9) is composed of 3 superimposed porosity areas at different layers, \(z=1.41\), \(1.69\), and \(1.98\) mm (see Figure 1d for reference). As a result, CNRmax values of defect 1 (Figure 8) are higher than those of defect 3 (Figure 9).