## 5 Conclusions  
In this paper, a new algorithm using a dual-path adversarial generation network model is proposed for the reconstruction of satellite remote sensing images. In the generator module, we utilized both the attention mechanism network and the enhanced deep convolutional network to extract the features from images, and we fused them in the reconstruction layer to generate high-resolution images. Moreover, the activation function and loss function were improved, such that the output image and discriminator could achieve a relatively optimal balance and restore a clear image. Additionally, we tested our algorithm on the public UCAS-AOD datasets, and then compared it with several relevant algorithms; our experimental results show that the algorithm in this paper could achieve a clear reconstruction of lines, edges, and details from satellite remote sensing images, thus greatly enhancing the quality of satellite remote sensing images. The average PSNR value was 32.64 dB on Dataset 4, which was 2.73 dB higher than on the SRCNN algorithm. The  
Figure 16: Comparison of values with different algorithms on the 3\(\times\) “P0808” airport picture: (**A**) LR; (**B**) bicubic; (**C**) SRCNN; (**D**) FSRCNN; (**E**) VDSR; (**F**) EDSR; (**G**) LapSRN; (**H**) SRGAN; (**I**) ours.  
Figure 15: Comparison of values with different algorithms on the 3\(\times\) “N0502” house picture: (**A**) LR; (**B**) bicubic; (**C**) SRCNN; (**D**) FSRCNN; (**E**) VDSR; (**F**) EDSR; (**G**) LapSRN; (**H**) SRGAN; (**I**) ours.  
improvement in data accuracy provided by our solution brings enormous performance potential for many practical applications such as satellite navigation, surface observation, mapping, and military reconnaissance. For the next step of our plan, deep learning technology can be applied to the field of satellite infrared remote sensing monitoring, to provide strong technical support for night military reconnaissance and intelligent clear imaging of space stations.  
Z.R., conceptualization, methodology, and writing; J.Z., supervision and review; C.C., validation; Y.L., training and testing; X.M., testing and review. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the Department of Science and Technology of Jilin Province] grant number [20220402014 GH], [the Education Department of Jilin Province] grant number [JKH20220774 KJ], [the Open Fund Project of the State Key Laboratory of Applied Optics] grant number [SKLAO2022001A07], [the Education and Science Planning Project of Jilin Province] grant number [GH22197], [the Natural Science Foundation of Jilin Province] grant number [YDZJ202201 ZYTS411].  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The dataset can be downloaded at the website: [[https://opendatalab.com/102/download](https://opendatalab.com/102/download)]([https://opendatalab.com/102/download](https://opendatalab.com/102/download)) (accessed on 16 July 2022).  
This research was funded by the 2022 International Cooperation Project in Jilin Province Science and Technology Department (grant number 20220402014 GH), the Science and Technology Research Project in Jilin Provincial Department of Education (grant number JKH20220774 KJ), the Open Fund Project of the State Key Laboratory of Applied Optics (grant number SKLAO2022001A07), and the Jilin Provincial Education Planning Project (grant number GH22197)].  
The authors declare no conflict of interest.