# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Data Fusion Method Evaluation
#### 4.2.2 Performance of Image Fusion Module  
This study validated the performance of the image fusion module by comparing it with advanced image fusion models. U2 Fusion and EMFusion models were selected as controls. In cladding processing, the interaction between powder and substrate when heated and melted, the melt pool flow, and the rapid solidification can lead to unstable reactions, and different temperature distributions in the melt pool directly affect the shape and microstructure of the formed material. Therefore, comprehensive consideration was given to the temperature distribution and the morphology of the melt pool in the fused images. As shown in Figure 11, other models weakened the source image information. The U2 Fusion model pays insufficient attention to temperature distribution, leading to inadequate retention of melt pool temperature characteristics and severe distortion of temperature gradient features. It also lacks attention to the morphology of the melt pool and surrounding information, resulting in a lack of texture details in the fused image. Although the EMFusion model retains the morphological features of the melt pool in visible-light images, it pays insufficient attention to temperature distribution information, leading to severe distortion and difficulty in accurately reflecting the temperature distribution. The image fusion module effectively retains the temperature distribution and morphological information of the melt pool, generating fused images rich in texture details and temperature values. At the same time, the data distortion caused by upsampling of infrared temperature distribution data is supplemented with a certain amount of information. In the low-temperature area at the center of the melt pool, where liquid metal fragments into high-temperature slag, the change in emissivity leads to inaccurate temperature detection. This phenomenon is mitigated in the fused images.  
Figure 10: Dynamic behavior of the melt pool captured by a co-axial high-speed camera.  
To present the results more intuitively, this study conducted a quantitative comparison of the three aforementioned models across five evaluation metrics, including PSNR [27], FMI [28], Q\({}_{\text{abf}}\)[29], MS-SSIM [30], N\({}_{\text{abf}}\)[31], AG [27], SD [32], and SF [33]. The results, as shown in Table 4, indicate that the proposed image fusion module achieved the best results for all metrics except PSNR. The optimal AG metric indicates that the fused images produced by the image fusion module are richer in detail and have higher clarity. The best performance in SD and SF indicates that the fused images generated by the image fusion module are of the highest quality and the clearest. The best results in Q\({}_{\text{abf}}\) and FMI indicate that the image fusion module retains more structural information and features from the original images. The optimal results of the MS-SSIM metric indicate that the image fusion module pays higher attention to multi-scale feature extraction, which provides significant potential for its application in the field of multi-scale defect detection. Due to the image fusion module's comprehensive integration of the global interactions of the source images, focusing more on important areas in infrared and visible-light images while ignoring less important areas, the resulting fused images have a lower PSNR compared to other models. The results indicate that our image fusion module performs optimally, generating fused images that enhance information extraction and fusion in the melt pool area while appropriately reducing information fusion in non-melt pool areas, thereby reducing the noise in the generated fused images.