## 4 Experimental Setup and Result Analysis
### Ablation Study  
#### 4.4.1 Ablation Study on Multi-Scale Feature Extraction and Optical Flow Module  
In this study, a wide-angle image dataset was constructed by applying radial distortion transformations to the Places2 dataset and was used as the experimental sample for both model training and performance evaluation.  
Experiment 2 aims to compare the network's performance before and after introducing key modules, with a particular focus on the trend of total loss during training. Figures 15 and 16 present the loss curve comparisons between the two configurations. It is evident that the incorporation of the multi-scale feature extraction module and the optical flow field module leads to a significant reduction in total loss. At epoch 50, the optimized model achieved a reduction of 0.3019 in total loss compared to the baseline model (denoted as BASE), indicating improved convergence efficiency and enhanced generalization capability.  
BASE  
BASE+Multi-Scale Module  
BASE+Optical Flow Module  
BASE+Multi-Scale Module+Optical Flow Module  
The performance differences of these models in correcting distorted wide-angle images are summarized in Table 1.  
Figure 16: Training loss curve of the model with the integration of multi-scale feature extraction and optical flow constraint modules. The \(x\)-axis represents the number of training epochs, and the \(y\)-axis denotes the total loss value.  
Figure 15: Training loss curve of the model without the integration of multi-scale feature extraction and optical flow constraint modules. The \(x\)-axis represents the number of training epochs, and the \(y\)-axis denotes the total loss value.  
This section further evaluates the effectiveness of integrating multi-scale feature extraction and optical flow modules into the baseline Transformer architecture (denoted as BASE). Four model variants were constructed based on this baseline, and their performance differences are summarized in Table 2. Comparative experiments were conducted on the Places2 dataset, MS-COCO, and real-world wide-angle surveillance images.  
Introducing the multi-scale feature extraction module alone resulted in a PSNR improvement from 22.21 dB to 25.36 dB (+3.15 dB), indicating that enhancing channel dimensionality and hierarchical depth significantly improves the model's ability to represent fine image details. The inclusion of the optical flow module further increased the PSNR to 26.97 dB, demonstrating its strong guidance effect in modeling the directional structure of image distortions. When both modules were jointly incorporated, the model achieved the best performance, with a PSNR of 27.43 dB and SSIM of 0.8190, representing improvements of +5.22 dB and +0.0808 over the BASE model, respectively.  
In addition, we evaluated the computational cost and inference efficiency of each model variant. As shown in the table, integrating both modules increased the parameter count from 26.85 M to 35.93 M and the FLOPs from 2.15T to 2.45T. Nonetheless, the average inference time was maintained at 9.5 ms, which satisfies real-time processing requirements (>10 FPS) on the Jetson TX2 NX platform. These results demonstrate that the proposed model achieves a favorable balance between performance gains and lightweight deployment suitability.