| Generative Adversarial Network-based Enhancement for Super-Resolution Reconstruction in Division of Focal Plane Images | |
Li, Shuo1,2; Wang, Weifeng1 ; Ji, Ran1,2; Luo, Zhanyi1,3
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| 2023 | |
| 会议名称 | 7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023 |
| 会议录名称 | 2023 7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023 |
| 页码 | 879-883 |
| 会议日期 | 2023-10-20 |
| 会议地点 | Hybrid, Xi'an, China |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| 产权排序 | 1 |
| 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. |
| 关键词 | Polarimetric imaging IFoV Super-Resolution construction SRGAN Deeeplearning Introduction |
| 作者部门 | 光学定向与测量技术研究室 |
| DOI | 10.1109/ICEMCE60359.2023.10490495 |
| 收录类别 | EI |
| ISBN号 | 9798350382877 |
| 语种 | 英语 |
| EI入藏号 | 20241816005707 |
| 引用统计 | |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/97445 |
| 专题 | 光学定向与测量技术研究室 |
| 通讯作者 | Wang, Weifeng |
| 作者单位 | 1.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China; 2.School of Optoelectronics, University of Chinese Academy of Sciences, Xi'an, China; 3.School of Physics and Information Technology, Shaanxi Normal University, 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]:Institute of Electrical and Electronics Engineers Inc.,2023:879-883. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Generative Adversari（712KB） | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 | ||
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