## 7 Conclusions  
This paper has three contributions. First of all, we reviewed the most recent works regarding the processing of CFRP samples tested by PT. Secondly, because dimensional reduction methods are very popular in IRNDT, we investigated the influence of the number of components on the results from a quantitative and qualitative perspective. We limited our study to PCT and several ICA methods selected among the recent works. We compared the CNR in each defect for when we used seven and hundred components. Our results show that for each method when one hundred components are computed the CNR per defect is not noticeably higher than for seven components. Similarly, the trends regarding the evaluation of the CNR per surface and depth are the same. From a detection point of view, having many components makes it harder to find accurate and reliable segmentation methods without supervised learning. Some strategies based on simple statistics, such as the interquartile range, or the kurtosis, may provide a quasi-accurate segmentation method with fewer components. In our third contribution, we quantitatively compare the selected ICAs algorithm with PCT and PPT. Our results show that in most of the cases ICAs method outperformed PPT, but not PCT. However, for smaller defects and deeper defects ICAs method often outperforms PCT. The results also show that if ICAs results are generally not far from the one of the PCT.  
Further research would involve supervised ICA algorithms, feature enhancement methods [15], and the unsupervised ICA method in this research on a wider range of materials.  
Conceptualization, J.R.F., S.E., C.I-C. and X.P.V.M.; methodology, J.R.F. and S.E.; software, J.R.F. and S.E.; validation, J.R.F. and S.E.; formal analysis, J.R.F. and S.E.; investigation, J.R.F. and S.E.; data, C.I.-C.; data curation, J.R.F. and S.E. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the NSERC DG program and Canada Research Chair Program (MIVIM Multipolar Infrared Vision Infrarouge Multipolaire).  
Data Availability StatementData sharing not applicable.  
The authors would like to thank [PERSON] for the time and energy she spent reviewing this article.  
The funders had no role in the design of the study; in the collection, analyses,or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
NDT Non Destructive Testing IRT InfraRed Thermography PT Pulsed Thermography PPT Pulsed Phase Thermography PCA Principal Component Analysis PCT Principal Component Thermography ICA Independent Component Analysis ICT Independent Component Thermography BSS Blind Signal Separation CFRP Carbon Fiber Reinforced Plastic CNR Contrast over Noise Ration MoG Mixture of Gaussian CCIPCA Candid Covariance-free Incremental Principal Component Analysis CCIPCT Candid Covariance-free Incremental Principal Component Thermography ESPCA Edge-group Sparse Principal Component Analysis ESPT Edge-group Sparse Principal Component Thermography Sparse-PCA Sparse Principal Component Analysis SPECT Sparse Principal Component Thermography NMF Non-negative Matrix Factorization LatLRR Latent Low Rank Representation LatLRRT Latent Low Rank Representation in Thermography PLS Partial Least Square PLST Partial Least Square Thermography TSR Thermographic Signal Reconstruction SVM Support Vector Machine