Single Space Object Image Super Resolution Reconstructing Using Convolutional Networks in Wavelet Transform Domain | |
Feng, Xubin1; Su, Xiuqin1; Xu, Zhengpu2; Xie, Meilin1; Liu, Peng1; Lian, Xuezheng1; Jing, Feng1; Cao, Yu1 | |
2020-05 | |
会议日期 | 2020-05-08 |
会议地点 | Chengdu, China |
关键词 | component convolutional neural network wavelet transform space object image |
DOI | 10.1109/ICET49382.2020.9119660 |
页码 | 862-866 |
英文摘要 | With the increasing importance of space exploration, the research of space object is becoming more and more important because high-quality space object images are meaning for space attack and defense confrontation. However, high-quality space object images are very difficult to obtain because of the large number of various rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging. Image super resolution reconstruction methods are the most cost-effective way to solve the problem. In this paper, we propose a deep convolutional neural network based method to improve the resolution of space object image. The implementation of our method is in wavelet transform domain rather than spatial domain because wavelet transformation could decompose different frequencies of the image very effectively and this could further more enhance the performance. The experiment result shows that our method could achieve a very good performance. © 2020 IEEE. |
产权排序 | 1 |
会议录 | 2020 IEEE 3rd International Conference on Electronics Technology, ICET 2020 |
会议录出版者 | Institute of Electrical and Electronics Engineers Inc. |
语种 | 英语 |
ISBN号 | 9781728162836 |
内容类型 | 会议论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/93586] |
专题 | 西安光学精密机械研究所_光电测量技术实验室 |
作者单位 | 1.Chinese Academy of Sciences, Photoelectric Tracking Xi'an, Institute of Optics and Precision Mechanics, Xi'an, China; 2.Xidian University, Computer Science, Xi'an, China |
推荐引用方式 GB/T 7714 | Feng, Xubin,Su, Xiuqin,Xu, Zhengpu,et al. Single Space Object Image Super Resolution Reconstructing Using Convolutional Networks in Wavelet Transform Domain[C]. 见:. Chengdu, China. 2020-05-08. |
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