# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 4 Results
### Ablation Study  
#### 4.4.1 Study of Ablation of Network Structures  
In this part of the study, we demonstrate the effectiveness of each module in the proposed MCFN and their contribution to the network performance. We design a series of ablation experiments, as shown in Table 2. We evaluate their contribution to network performance by adding or replacing critical modules in the network. Firstly, we construct a base network consisting of a series of PMMs, called the PMMs network. The base network adopts a multi-scale mechanism of depth-separable convolution and pointwise convolution, improving computational efficiency while ensuring adequate feature information extraction at different scales. Then, IAEM was added to our study to evaluate the network performance of PMMs, denoted as MTMs_PMMs + IAEM. Subsequently, CFMs were added to the PMMs to assess the effect of the addition on the network's performance, denoted as MTMs (PMMs + CFM). It is worth noting that we did not perform ablation experiments on the combination of PMMs and CFM alone. Instead, we chose to perform ablation experiments on MTMs (a combination of PMMs and CFMs) together with IAEM, aiming to assess the impact of CFM on performance in the presence of IAEM. Therefore, we used the strategy of replacing CFMs with PAMs and CAMs, denoted as MTMs_PAM + IAEM and MTMs_CAM + IAEM, and similarly, in order to assess the performance of IAEMs, we replaced IAEMs with CAMs in the MCFN structure, denoted as MTMs + CAM. Although this design scheme for ablation experiments may be different from traditional ablation methods, it provides us with an effective way to assess the interactions of the individual modules. In addition, this design approach aligns more with our experimental resource realities, allowing us to perform the most effective performance evaluation under limited conditions. We select most of the modeling methods, PSNR, and SSIM values on Set5, Set14, and B100 test sets for 200 cycles of comparison to ensure the necessity and validity of the experiments. In order to show the experimental results more intuitively, we plotted the experimental data of the last 50 cycles as a line graph-Figure 9.  
Figure 8: Visual comparison of the proposed method with other methods in terms of letters (\(\times\)4). Table 2 and Figure 9 demonstrate a clear trend: adding the fusion and enhancement networks to the base network significantly improves the network's performance metrics, proving the effectiveness of the individual modules and indicating that better results can be obtained. The performance improvement is pronounced in the MTMs_CAM + IAEM network. This network effectively focuses on critical feature information by learning the relevance between different channels, which demonstrates the importance of correlation learning after deep extraction of high-frequency information. In particular, in the MCFN network, we design an innovative cross-attention fusion module. This network not only effectively learns the spatial locations of shallow feature information through the cross-module learning approach but also combines this spatial location feature information with deep feature information through the cross-connection strategy to deeply learn the relevance of the information in the channel. This hierarchical approach improves the comprehensiveness of information utilization. In CFM, by integrating spatial and channel features, we achieve a more comprehensive fusion of information, enabling the network to achieve the best results in several performance metrics. When analyzing the performance of IAEM, we used CAM as a control group to learn the difference in performance between the two. As shown in Table 2 and Figure 9, our network performs better in PSNR and SSIM than the control group in the above test set experimental results. The results of the above analyses demonstrate the effectiveness of our module in performing relevant learning.