## 5 Conclusions  
In this study, we used SDO/AIA images as low-resolution inputs and Hi-C images as high-resolution targets to prepare datasets through image registration technology, creating paired images that are closer to real-world application scenarios. We improved the ESRGAN algorithm and proposed a new model named SAFCSRGAN, which we applied to the super-resolution reconstruction task of SDO/AIA images. Although the number of parameters in the model was reduced, the performance of our model has been enhanced. Experimental evidence shows that our improved model outperforms other common convolutional neural network super-resolution models in terms of performance. On image quality assessment metrics, our model has shown improvements in PSNR and SSIM indices compared to the original model. Additionally, on image perceptual quality assessment indices such as NIQE and LPIPS, our model also displayed superior performance compared to other models. Particularly in the application of super-resolution solar astronomical images, our model demonstrates better performance in reconstructing the texture details of images and produces a lower pixel difference rate compared to other models.  
Super-resolution (SR) technology faces certain challenges when dealing with solar images. Our research indicates that deep learning-based models can be effectively used for super-resolution reconstruction of astronomical images, as they are able to recover the original texture details of the images well. Super-resolution images help us to study small-scale details in astronomical images, such as coronal morphological features and solar sunspot structures. However, limited by the scale of the dataset, the variety of images, and the performance of the models, there might be biases in the super-resolution effect for certain types of coronal images. In the future, we could consider expanding the dataset using images acquired by other coronal telescopes to enhance the model's generalization capability.  
We would like to express our gratitude to the NASA SDO/AIA scientific team and the High-Resolution Coronal Imager (Hi-C) instrument team for providing the data.  
Figure 11: Set the proportion of different pixels to total image pixels under different difference thresholds. Compared to the baseline model, ours has fewer points of discrepancy, indicating that the processed corona images are more similar to the original images.  
**Author Contributions:** Conceptualization, Z.S.; funding acquisition; Z.S.; supervision, Z.S.; writing--review and editing, Z.S.; methodology, R.L.; software, R.L.; visualization, R.L.; writing--original draft, R.L.; validation, R.L.; formal analysis, R.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 12063002.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All our research data are from public data. Hi-C data comes from [[https://hic.msfc.nasa.gov/data_products.html](https://hic.msfc.nasa.gov/data_products.html)]([https://hic.msfc.nasa.gov/data_products.html](https://hic.msfc.nasa.gov/data_products.html)), and SDO/AIA data comes from [[http://soc.stanford.edu/data/aia/images/](http://soc.stanford.edu/data/aia/images/)]([http://soc.stanford.edu/data/aia/images/](http://soc.stanford.edu/data/aia/images/)). If you need the training set and test set data of this study, you can ask the corresponding author for it.  
**Conflicts of Interest:** The authors declare no conflict of interest.