NSSC OpenIR  > 空间技术部
Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression
Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang; Yu, Qiang (yuqiang@nssc.ac.cn)
作者部门空间技术部
发表期刊Microgravity Science and Technology
2018
卷号30期号:3页码:321-329
ISSN0938-0108
语种英语
关键词Containerless Microgravity Drop Tube Image Super Resolution
摘要Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang’s related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image’s edge blurs. © 2018 Springer Science+Business Media B.V., part of Springer Nature
收录类别SCI ; EI
文献类型期刊论文
条目标识符http://ir.nssc.ac.cn/handle/122/6196
专题空间技术部
通讯作者Yu, Qiang (yuqiang@nssc.ac.cn)
推荐引用方式
GB/T 7714
Zou, Zhenzhen,Luo, Xinghong,Yu, Qiang,et al. Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression[J]. Microgravity Science and Technology,2018,30(3):321-329.
APA Zou, Zhenzhen,Luo, Xinghong,Yu, Qiang,&Yu, Qiang .(2018).Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression.Microgravity Science and Technology,30(3),321-329.
MLA Zou, Zhenzhen,et al."Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression".Microgravity Science and Technology 30.3(2018):321-329.
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
201812217-018-9597-6(997KB)期刊论文作者接受稿开放获取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
[Zou, Zhenzhen]的文章
[Luo, Xinghong]的文章
[Yu, Qiang]的文章
百度学术
百度学术中相似的文章
[Zou, Zhenzhen]的文章
[Luo, Xinghong]的文章
[Yu, Qiang]的文章
必应学术
必应学术中相似的文章
[Zou, Zhenzhen]的文章
[Luo, Xinghong]的文章
[Yu, Qiang]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。