# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 5 Conclusions  
We propose a super-resolution algorithm based on the Enhanced Generative Adversarial Network (EGAN). To better learn the semantic features and to generate image representations, the Laplacian pyramid and convolutional skip-convolutions frameworks have been accepted. It is significant for the generative model to generate a plausible-looking image. In addition, we also present a perceptual loss function, which can further refine high-frequency details. Quantitative and qualitative evaluations in Tables 3 and 4 have demonstrated that the proposed method performs better than other algorithms at the same speed. For example, our algorithm achieves a 0.6 dB higher average PSNR than the SRGAN, within the same SR time. Meanwhile, EGAN is also a fast method, and is twice as fast as DRRN in reaching a stable point. Therefore, our algorithm achieves the best score, considering both speed and performance. Nevertheless, our algorithm has some limitations, such as the production of some nonsensical outputs, and it is unstable to train. These problems will be studied in our future work.  
[PERSON] contributed to the conception of the study. [PERSON] performed the experiments and wrote the manuscript. [PERSON] contributed significantly to the analysis and manuscript preparation. Lastly, [PERSON] assisted with performing the analysis, with constructive discussions. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
The training and test datasets are public datasets downloaded from the network. The URLs are follows: Set5: [[http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html)]([http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html)) (accessed on 17 August 2021). Set14: [[https://sites.google.com/site/romanzeyde/research-interests](https://sites.google.com/site/romanzeyde/research-interests)]([https://sites.google.com/site/romanzeyde/research-interests](https://sites.google.com/site/romanzeyde/research-interests)) (accessed on 17 August 2021). Urban100: [[https://sites.google.com/site/fbuang0604/publications/struct_sr](https://sites.google.com/site/fbuang0604/publications/struct_sr)]([https://sites.google.com/site/fbuang0604/publications/struct_sr](https://sites.google.com/site/fbuang0604/publications/struct_sr)) (accessed on 17 August 2021). BSD100: [[https://www.eecs.berkeleyedu/Research/Projects/CS/vision/bsds/](https://www.eecs.berkeleyedu/Research/Projects/CS/vision/bsds/)]([https://www.eecs.berkeleyedu/Research/Projects/CS/vision/bsds/](https://www.eecs.berkeleyedu/Research/Projects/CS/vision/bsds/)) (accessed on 17 August 2021).  
A great deal of support and assistance was received from [PERSON] and [PERSON], they provided valuable guidance and suggestions, and successfully completed this manuscript. Additionally, [PERSON] performed the experiment guidance and parameter optimization. At last, the experiment could not have operated smoothly without the device provided by the Aviation Cluster Warfare Laboratory of Air Force Engineering University.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SR & Super-Resolution \\ GANs & Generative Adversarial Networks \\ EGAN & Enhanced Generative Adversarial Networks \\ CNNs & Convolutional Neural Networks \\ HR & High-resolution \\ LR & Low-resolution \\ SRCNN & Super-Resolution Convolutional Neural Network \\ LapSRN & Deep Laplacian Pyramid Network \\ DRRN & Deep Recursive Residual Network \\ VAE & Variational AutoEncoders \\ FSRCNN & Fast Super Resolution Convolutional Neural Networks \\ VDSR & Very Deep Convolutional Network \\ \end{tabular}