## 4 Results
### CFRP\({}_{\text{FBH}}\) Coupon
With ASI, only one single damage map is obtained, which serves for both defect detection and quantitative defect depth inversion (based on calibration curves from simulation) [19]. Next, in order to provide a quantitative measure of the defect detectability, the contrast-to-noise ratio (CNR) was calculated for each detected FBH through the following formula [30; 37]:  
\[CNR=20\log_{10}\left(\frac{\left|\overline{A}_{d}-\overline{A}_{ref}\right|}{ \sigma_{A_{ref}}}\right)\left(\mathrm{d}\mathrm{B}\right) \tag{5}\]  
where \(\overline{A}_{d}\) and \(\overline{A}_{ref}\) are the average values of the defective and reference area, respectively. Squares of 16 pixels \(\times\) 16 pixels were selected at the FBH centers for the defective areas \(A_{d}\), and the reference area \(A_{ref}\) was defined by a rectangle of 176 pixels \(\times\) 31 pixels in the center of the sample (illustrated as \(A_{ref}\) in Figure 5a). The CNR values of all FBHs are reported in Table 1 for the post-processing results that were magnitude based (i.e., not DTT's timegram and FDT's frequencygram). As expected, the CNR values decreased with increasing defect depth. DTT's \(\mathrm{m}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{T}\) provided the lowest CNR value of all techniques, and only detected the three shallowest FBHs. The negative CNR for the deepest two FBHs indicates that they were not detected. TSA provides a significant improvement over the \(\mathrm{m}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{T}\). While \(\mathrm{FB}\mathrm{e}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{T}\) was not visually detected (at 4.51 mm, see Figure 5c), its CNR value indicates that it can be extracted by TSA. FDT's \(\mathrm{m}\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{T}\) consistently provided an increased defect detectability in comparison to TSA. ASI obtained an even better defect detectability for the shallowest four FBHs, after which both frequency-domain approaches provide similar defect detectability for the deepest \(\mathrm{FB}\mathrm{e}\mathrm{i}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{T}\). The observation that both frequency-domain techniques' CNR values came closer together with increasing defect depth was explained by the fact that the second blind frequency \(f_{blind,2}\) (upper integration limit for ASI, see Figure 4c) reduced with increasing defect depth (see also Equation (3)). Thus, ASI's integration range gradually became smaller and finally, for very deep defects, effectively reduced to FDT's \(f_{\Delta\rho,max}\) due to the limited frequency resolution. Since CNR is strongly dependent on the noise level of the reference area (see Equation (5)), it does not always give a clear indication of how well a defect is actually detected. Defect-to-background ratio (DBR) is an alternative defect detectability metric that is less affected by measurement noise. First, the data is normalized so that the values range from 0 to 1 (this requires a sign inversion for FDT and ASBI), after which the DBR was calculated as follows [38]:  
\[DBR=\frac{\overline{A}_{d}}{\overline{A}_{ref}} \tag{6}\]  
The same reference and defect areas are used as for the CNR calculations. As such, DBR compares the ratio of the mean amplitudes of the reference and defected area. In principle, a defect that has a DBR value higher than 1 presents enough signal to be detectable. The DBR values for all FBHs and amplitude-based post-processing techniques are presented in Table 2. The DBR values for DTT's \(\text{maxigram}^{\text{T}}\) indicate that the three shallowest FBHs (depth up to 2.51 mm) were detectable, while the deepest were more difficult to detect. TSA did not give high DBR values, even though several FBHs were easily detectable in the output image (see Figure 5c).