## 3 Materials and Methods
### Methodology  
This study explores the potential of LVT for detecting BVID in CFRP materials by combining thermal image enhancement and quantitative evaluation techniques. The workflowis structured into three main stages: data acquisition, image processing, and performance assessment using SNR analysis.  
To start, thermal data were collected using an LVT setup. A 28 kHz piezoelectric transducer was used to apply continuous ultrasonic excitation to the CFRP specimens. A FLIR T560 long-wave infrared (LWIR) camera, operating at 30 Hz, recorded the resulting surface thermal response. The captured thermal video was converted into grayscale image frames for further processing.  
In the image processing stage, the data were analysed using two enhancement techniques, PCA and RPCA. These methods are designed to suppress variations in the thermal response of the sound area (i.e., undamaged regions within the specimen) and enhance the visibility of subtle thermal anomalies associated with damage.  
For performance evaluation, the effectiveness of each image processing method was quantified using SNR metric. An 80 \(\times\) 80 pixel region of interest (ROI) was manually selected to avoid areas too close to the ultrasonic transducer, as localised heating from the transducer itself could interfere with defect contrast and bias the PCA and RPCA results. For SNR calculation, a rectangular patch corresponding to the defect area was placed within the visible defect, while a separate patch was designated in a sound (undamaged) area of the specimen. Both defect and sound patch locations remained identical for PCA and RPCA to facilitate strict and objective comparison. This approach allowed for consistent, objective comparison of defect visibility across different enhancement methods.  
To validate the experimental results and benchmark the LVT approach, additional datasets from pulsed thermography (PT) and phased array ultrasonic testing (PAUT) were included. These PT and PAUT datasets were previously obtained and described by [PERSON] et al. (2025) [39], who is a co-author of the present study.  
The PT measurements were performed using a high-sensitivity cooled infrared camera (FLIR X8501 sc, InSb, spectral range 3-5 um, 50 mm focal length, 1280 \(\times\) 1024 pixels, NEdT \(<\) 20 mK, linear Stirling cooling). Unlike the LVT experiments, where two CFRP samples were tested simultaneously within the setup, PT measurements were performed on a single-sample basis. Two xenon flash lamps (Balcar, 2 ms pulse duration at FWHM, 6.2 kJ per flash) served as the excitation source, with all components synchronised via a PC. For each measurement, the camera was placed approximately 40 cm from the specimen to ensure full surface coverage. The two flashes were positioned 10 cm behind the camera, separated by a distance of 50 cm, and angled at 45 degrees with respect to the sample surface [39].  
The PT thermal data were processed using the same PCA and RPCA techniques as applied to the LVT data, while the PAUT data served as a reference for confirming the presence and location of subsurface defects.  
The PAUT was carried out using a Sonatest PA X3 system with a 64-element linear array transducer operating at 10 MHz and an element pitch of 0.6 mm. A flat elastomer wedge was used to maintain consistent contact with the specimen. For each scan, eight elements were activated as the aperture, which allowed the ultrasonic beam to be focused and steered as needed. Waveform data were collected at a sampling frequency of 125 MHz to ensure high temporal resolution [39].  
This combined approach enables a comparative evaluation of LVT's performance relative to established inspection methods, particularly in terms of thermal contrast and defect detectability. A summary of the methodology is illustrated in Figure 3.