## 5 Conclusions and Future Research  
This study conducted a study on thermal equalization of S275 specimens used in CLPs using the optical IRT technique. For thermal equalization, the Gaussian filtering-based  
Figure 26: SNR graph of amplitude image based on FFT algorithm applying top left thermal excitation. (a) top left; (b) 1.0; (c) 3.0; (b,c) refer to Gaussian weights.  
Figure 25: SNR graph of amplitude image based on FFT algorithm applying left side thermal excitation. (a) left side; (b) 1.0; (c) 3.0; (b,c) refer to Gaussian weights.  
FFT algorithm was applied. The study used two types of heat sources, long pulse and sine wave, and three locations of the heat sources: center, left side, and top left.  
First, thermal images were analyzed by exciting a long pulse heat source. When the FFT algorithm was applied to the thermal image of the A-type specimen, as the Gaussian weight increased, the effect of the thermal equalization increased. However, as the thermal equalization effect increased in the B-type specimen, the detectability decreased.  
Second, the phase image of the LIT technique was analyzed by exciting a sine wave heat source. When the FFT algorithm was applied, a pattern occurred in which noise was concentrated in the center as the Gaussian weight increased. As a result, detection is possible due to the boundary line of the defective area, but when analyzing the graph, the contrast between the defective area and the sound area is not clear due to noise.  
Third, the amplitude image of the LIT technique was analyzed by exciting a sine wave heat source. As the Gaussian weight increases, the high-frequency component moves to the side, creating a clear contrast between the defective area and the sound area. The effect of the FFT algorithm can be clearly seen in the binarized image acquired using the Otsu algorithm. Additionally, when comparing the SNR for the three heat source types, high performance can be seen on the left side and top left when an uneven heat source occurs on the side.  
The noise present in thermal images can be effectively processed through Gaussian filtering, and at the same time, it is considered a very effective mechanism as it can resolve uneven thermal distribution. In addition, the thermal images acquired through the eddy current thermography (ECT) technique show localized heat concentration at the corners due to [PERSON]'s heating. Therefore, it can be expected that excellent effects will be obtained by applying the FFT algorithm presented in this study. Future research will perform super-resolution conversion using the very deep super-resolution (VDSR) deep neural network to remove noise and improve the blurring phenomenon caused by applying the FFT algorithm based on the data of this study.  
Conceptualization, S.L. and W.K.; methodology, Y.C.; software, S.L.; validation, S.L., Y.C. and H.S.; formal analysis, H.S.; investigation, S.L.; resources, Y.C.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, W.K. and H.S.; visualization, Y.C.; supervision, W.K. and H.S.; project administration, W.K.; funding acquisition, W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Development Program (S3249835) funded by the Ministry of SMEs and Startups (MSS, Korea).  
Not applicable.  
The data are only available upon reasonable request.  
The authors declare no conflicts of interest.