中国科学院国家空间科学中心机构知识库
Advanced  
NSSC OpenIR  > 空间技术部  > 期刊论文
题名: Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression
作者: Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang
作者部门: 空间技术部
通讯作者: Yu, Qiang (yuqiang@nssc.ac.cn)
关键词: Containerless ; Microgravity ; Drop tube ; Image super resolution
刊名: Microgravity Science and Technology
ISSN号: 0938-0108
出版日期: 2018
卷号: 30, 期号:3, 页码:321-329
收录类别: SCI ; EI
英文摘要: 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
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.nssc.ac.cn/handle/122/6196
Appears in Collections:空间技术部_期刊论文

Files in This Item:
File Name/ File Size Content Type Version Access License
201812217-018-9597-6.pdf(997KB)期刊论文作者接受稿限制开放View 联系获取全文

Recommended Citation:
Zou, Zhenzhen,Luo, Xinghong,Yu, Qiang. Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression[J]. Microgravity Science and Technology,2018,30(3):321-329.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zou, Zhenzhen]'s Articles
[Luo, Xinghong]'s Articles
[Yu, Qiang]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zou, Zhenzhen]‘s Articles
[Luo, Xinghong]‘s Articles
[Yu, Qiang]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: 201812217-018-9597-6.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2018  中国科学院国家空间科学中心 - Feedback
Powered by CSpace