## 3 Results and Analysis
### Ablation Experiment  
We have made critical improvements to the ESRGAN network. Firstly, an attention mechanism was introduced, which is particularly important for coronal images because it allows the model to capture coronal features more effectively. After the sixth RRCAB module in the network, we implemented feature activation visualization. Compared to the original model, our improved model demonstrates superior performance in capturing overall solar coronal features. As shown in Figure 9, the feature activation maps indicate that the introduction of the spatial attention mechanism significantly enhances feature recognition capability. Based on this, the model we propose not only further extends the range of capturing coronal features but also captures more coronal feature information that the original model failed to recognize.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **NIQE\(\downarrow\)** & **LPIPS\(\downarrow\)** \\ \hline Bicubic & 29.0103 & 0.7328 & 10.8501 & 0.3964 \\ ESRGAN (Baseline) & 32.9796 & 0.8762 & 5.9583 & 0.1835 \\ SRCNN & 30.0458 & 0.7891 & 9.9584 & 0.3781 \\ EDSR & 36.9069 & 0.9132 & 9.8624 & 0.3538 \\ RCAN & 36.4203 & 0.9030 & 9.7381 & 0.3562 \\ SRGAN & 28.5083 & 0.7796 & 7.2463 & 0.2954 \\ MSRResNet & 36.8074 & 0.9114 & 9.5365 & 0.3514 \\ Ours & 36.9125 & 0.9183 & 5.3158 & 0.1812 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of image quality assessment metrics between ours and other models. In the super-resolution processing effect on solar corona images, ours outperforms other models in both pixel-level and perceptual metrics.  
Figure 8: _Cont._  
Secondly, we redesigned the architecture of the basic blocks while retaining some dense connections. We believe that a moderate amount of dense connections can enhance the utilization of shallow features, but the originally excessive dense connections led to the production of redundant features, which reduced the efficiency of the model's feature extraction. The improved model not only achieved better performance in terms of image super-resolution metrics, but also the number of model parameters was reduced by 21%.  
Figure 8: Comparison of super-resolution details among different models. In (**a**,**c**), our method achieves the best performance in terms of the LPIPS metric, and in (**c**), our method does not have artifacts. In (**b**,**d**), our method strikes a good balance between pixel-level metrics and image perception metrics, with near-optimal values on pixel image metrics while also surpassing other models in terms of perceptual quality metrics.  
Figure 9: Compare the feature activation maps of our model with the baseline model that incorporates the attention mechanism. Our model exhibits a broader scope and stronger focus on the features of corona images.  
As shown in Table 2, we removed some of the basic blocks with dense connections as part of the model's main body. As a result, the number of parameters was reduced to 13.02 M, yet the performance of the model was enhanced. This result confirms our hypothesis: an excess of dense connections, while increasing the amount of learnable parameters of the model actually limited the model's performance. In experiments, we observed that whether it was channel attention or spatial attention across merging channels, both significantly improved the model's metrics.