Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

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	Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression
	Zou, ZZ; Luo, XH; Yu, Q; Yu, Q (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China.
刊名	MICROGRAVITY SCIENCE AND TECHNOLOGY
	2018-05-01
卷号	30 期号:3 页码:321-329
关键词	Superresolution Microgravity Alloy
ISSN号	0938-0108
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 10(6) frames/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.; 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 10(6) frames/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.
学科主题	Engineering, Aerospace ; Thermodynamics ; Mechanics
语种	英语
公开日期	2018-06-05
内容类型	期刊论文
源URL	[http://ir.imr.ac.cn/handle/321006/79318]
专题	金属研究所_中国科学院金属研究所
通讯作者	Yu, Q (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China.
推荐引用方式 GB/T 7714	Zou, ZZ,Luo, XH,Yu, Q,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, ZZ,Luo, XH,Yu, Q,&Yu, Q .(2018).Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression.MICROGRAVITY SCIENCE AND TECHNOLOGY,30(3),321-329.
MLA	Zou, ZZ,et al."Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression".MICROGRAVITY SCIENCE AND TECHNOLOGY 30.3(2018):321-329.