## 3 Data Processing and Image Elaboration Procedures  
The thermal contrast is the chosen parameter for defect localization by means of pulsed thermography; the surface temperature contrast is used to better investigate defect detectability and offers a qualitative comparison between standard defect and real delamination. Automatic advanced image-processing with preliminary algorithm is implemented in the MATLAB R2014b (The MathWorks, Inc., Natick, MA, USA, 2014) environment, to upload the three-dimensional matrix of thermal frames and to return selected thermal maps of specimen for various tests; local contrast is determined, highlighting various heat accumulation sites obtained over time, intact zones, and damaged areas [20] processed together with the absolute contrast parameter \(C_{A}\), defined by the equation  
\[C_{A}(t)=T_{DZ}-\ T_{IZ} \tag{1}\]  
where the two terms \(T_{DZ}\) and \(T_{IZ}\) are the mean temperature in defect and intact zones during the cooling phase. Contrast analysis has limitation due to the used set-up and the operator's choice of reference intact areas for the contrast computation, for which following applied examples show some variability of results and correct definition of suitable intact reference zone for each selected defect is time consuming, especially if implemented in automated analysis.  
The authors suggest developing an algorithm with different approach, to possibly improve defect and visibility defect shape on the base of absolute contrast approach. Preliminary study is conducted to investigate defect by raw temperatures analysis in a chosen thermal frame; defect characterization and automated contrast mapping with accurate methodology represent the main purpose of paper.  
In this work, temperature perturbations as function of variable time is not considered for simplicity and a contrast enhance algorithm is proposed to map his values, as distributed completely on whole inspected specimen. This enhanced thermal contrast algorithm automates the simultaneous detection and mapping of contrast as optimized to identify defect boundaries according only to spatial variations in neighboring of each \([i,j]^{th}\) calculation point in the whole frame; in other words, the local contrast is automatically determined by calculating in the surroundings of a highest temperature spot in pre-defined areas the temperature variation \(\Delta T\). For a general location \([i,j]\) on the thermal image, the new contrast parameter \(C\) is defined through the formula  
\[C_{(i,j)}=\Delta T_{(i,\ j)}=\left|\overline{T_{D_{(i,j)}}}-\overline{T_{IZ_{( i,j)}}}\right| \tag{2}\]  
where, for each pixel of the thermographic image, the implemented MATLAB algorithmworks the differences of the two mean temperatures \(T_{D(i,j)}\) of the presumed defect in \([i,j]^{th}\) calculation point and \(T_{IZ(i,j)}\) temperature, calculated as mean value in a reference rectangular zone around \([i,j]^{th}\) calculation point. The new algorithm is based on basic principle that reference temperatures evaluated around the inspected spot tends to maximize different values when trepassing the defect border, leading to local contrast variations to be displayed, clearly distinguishing defect shape and boundaries.  
\begin{table}
\begin{tabular}{c c c c c} \hline N\({}^{\circ}\)**Test** & **Frame Rate [Hz]** & **Heating Time [s]** & **Acquisition Time [s]** & **Total Frames** \\ \hline
1 & 5 & 20 & 100 & 500 \\
2 & 5 & 30 & 100 & 500 \\
3 & 5 & 40 & 100 & 500 \\
4 & 5 & 12 & 200 & 1000 \\
5 & 5 & 15 & 200 & 1000 \\ \hline \end{tabular}
\end{table}
Table 5: Scheduling tests for the multi-stringer component’s investigation.  
The algorithm processes temperatures in iterative manner over the whole thermal map, without choosing an intact reference zone. This elaboration was inspired by DIC technique, where the correlation between the pixels does not occur in a temporal way, but punctual way, highlighting the maximum contrast reached in the proximity of defect border.