Generative Adversarial Network-based Enhancement for Super-Resolution Reconstruction in Division of Focal Plane Images
文献类型：会议论文
| 作者 | Li, Shuo2,3; Wang, Weifeng3 ; Ji, Ran2,3; Luo, Zhanyi1,3
|
| 出版日期 | 2023 |
| 会议日期 | 2023-10-20 |
| 会议地点 | Hybrid, Xi'an, China |
| 关键词 | Polarimetric imaging IFoV Super-Resolution construction SRGAN Deeeplearning Introduction |
| DOI | 10.1109/ICEMCE60359.2023.10490495 |
| 页码 | 879-883 |
| 英文摘要 | Advancements in technology have refined polarization imaging systems for realtime, multi-directional imaging. However, their super-pixel design leads to instantaneous field of view (IFoV) issues. Addressing this, a super-resolution method using the Super-Resolution Generative Adversarial Network (SRGAN) has been introduced. This method efficiently recovers high-quality details from low-resolution polarimetric images. Using PSNR and SSIM metrics, this method demonstrates enhanced performance over existing techniques. © 2023 IEEE. |
| 产权排序 | 1 |
| 会议录 | 2023 7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023
 |
| 会议录出版者 | Institute of Electrical and Electronics Engineers Inc. |
| 语种 | 英语 |
| ISBN号 | 9798350382877 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/97445]  |
| 专题 | 西安光学精密机械研究所_光学定向与测量技术研究室 |
| 通讯作者 | Wang, Weifeng |
| 作者单位 | 1.School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China 2.School of Optoelectronics, University of Chinese Academy of Sciences, Xi'an, China; 3.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China; |
| 推荐引用方式 GB/T 7714 | Li, Shuo,Wang, Weifeng,Ji, Ran,et al. Generative Adversarial Network-based Enhancement for Super-Resolution Reconstruction in Division of Focal Plane Images[C]. 见:. Hybrid, Xi'an, China. 2023-10-20. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。