OPT OpenIR  > 光电跟踪与测量技术研究室
Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks
Feng, Xubin1,2; Su, Xiuqin1,2; Shen, Junge3; Jin, Humin1
作者部门光电跟踪与测量技术研究室
2019-08
发表期刊REMOTE SENSING
ISSN2072-4292
卷号11期号:16
产权排序1
摘要

Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging, most of the images obtained are blurred and contain a lot of cosmic-ray noise. So, denoising methods and super-resolution methods are two effective ways to reconstruct high-quality space object images. However, most super-resolution methods could only reconstruct the lost details of low spatial resolution images, but could not remove noise. On the other hand, most denoising methods especially cosmic-ray denoising methods could not reconstruct high-resolution details. So in this paper, a deep convolutional neural network (CNN)-based single space object image denoising and super-resolution reconstruction method is presented. The noise is removed and the lost details of the low spatial resolution image are well reconstructed based on one very deep CNN-based network, which combines global residual learning and local residual learning. Based on a dataset of satellite images, experimental results demonstrate the feasibility of our proposed method in enhancing the spatial resolution and removing the noise of the space objects images.

关键词space object cosmic-ray denoising super-resolution CNN residual learning
DOI10.3390/rs11161910
收录类别SCI
语种英语
WOS记录号WOS:000484387600073
出版者MDPI
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/31857
专题光电跟踪与测量技术研究室
通讯作者Shen, Junge
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714
Feng, Xubin,Su, Xiuqin,Shen, Junge,et al. Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks[J]. REMOTE SENSING,2019,11(16).
APA Feng, Xubin,Su, Xiuqin,Shen, Junge,&Jin, Humin.(2019).Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks.REMOTE SENSING,11(16).
MLA Feng, Xubin,et al."Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks".REMOTE SENSING 11.16(2019).
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
Single Space Object (1556KB)期刊论文出版稿限制开放CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Feng, Xubin]的文章
[Su, Xiuqin]的文章
[Shen, Junge]的文章
百度学术
百度学术中相似的文章
[Feng, Xubin]的文章
[Su, Xiuqin]的文章
[Shen, Junge]的文章
必应学术
必应学术中相似的文章
[Feng, Xubin]的文章
[Su, Xiuqin]的文章
[Shen, Junge]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

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。