# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 3 A Method for Multi-Source Data Fusion Based on Video Frame Interpolation  
### Infrared Video Frame Interpolation Network Model  
In current research on melt pool temperature distribution based on infrared thermal cameras, there is an issue where the frame rate of infrared video is low while that of visible-light video is high. To balance the frame rates and supplement the dynamic changes in infrared video, this study designed a deep learning-based video frame interpolation module. Deep learning video frame interpolation models can be divided into optical flow methods and non-optical flow methods [12]. Optical flow methods calculate the motion information of objects by analyzing the temporal changes and inter-frame correlations of pixels in image sequences [17]. However, the characteristics of laser cladding melt pool images violate the fundamental principle of optical flow estimation, which is that the brightness of the same target remains constant across different frames. Therefore, this study employed non-optical flow methods. Currently, research on video frame interpolation based on non-optical flow methods has conducted in-depth analyses of issues such as large motion and occlusion and has introduced network structures such as 3D convolution [18], self-attention mechanisms [19], and deformable convolution networks [20]. Due to the rapid and non-linear changes in melt pool infrared videos, which are highly dependent \begin{table}
\begin{tabular}{c c c c} \hline
**Datasets** & **Types** & **Melt Pool Temperature Distribution** & **Visible-Light Melt Pool Morphology** \\ \hline \multirow{3}{*}{Dataset 1} & Training Set & 2220 & 2220 \\  & Validation Set & 278 & 278 \\  & Test Set & 278 & 278 \\ \hline \multirow{3}{*}{Dataset 2} & Training Set & 4440 & 4440 \\  & Validation Set & 556 & 556 \\  & Test Set & 556 & 556 \\ \hline \multirow{3}{*}{Dataset 3} & Training Set & 2220 & 4440 \\  & Validation Set & 278 & 556 \\  & Test Set & 278 & 556 \\ \hline \multirow{3}{*}{Dataset 4} & Training Set & 1110 & / \\  & Validation Set & 139 & / \\ \cline{1-1}  & Test Set & 139 & / \\ \hline \end{tabular}
\end{table} 
Table 1: Dataset details. on the spatiotemporal correlations of the melt pool motion, 3D space-time convolutions can learn the spatiotemporal features of melt pool infrared videos through convolution of multiple frames, generating interpolated frames with reasonable motion characteristics [21]. [PERSON] et al. [18] proposed a video frame interpolation model based on 3D space-time convolutions, known as the FLAVR model. The model is capable of accurately obtaining the spatiotemporal features of video data without optical flow, learning the linear motion characteristics of the target, and generating high-confidence interpolated frames based solely on the original video in the absence of reference data. However, the motion of the melt pool exhibits non-linear high-dynamic characteristics and is susceptible to image noise in high-temperature environments. To address these issues, this study proposes the Melt Pool Thermal Video Frame Interpolation (MTVFI) model, the architecture of which is shown in Figure 3. A loss function based on the Structural Similarity Index Measure (SSIM) is designed to constrain the model to learn the non-linear motion features of the melt pool and reduce image noise. Moreover, considering the characteristics of multi-source data, a training strategy based on transfer learning is proposed in this study. Through an end-to-end spatiotemporal feature learning mechanism, the model achieves high-confidence frame interpolation without the need for explicit optical flow estimation, making it suitable for melt pool monitoring in metal additive manufacturing where reference values are lacking.