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Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression
Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang; Yu, Qiang (yuqiang@nssc.ac.cn)
Department空间技术部
Source PublicationMicrogravity Science and Technology
2018
Volume30Issue:3Pages:321-329
ISSN0938-0108
Language英语
KeywordContainerless Microgravity Drop Tube Image Super Resolution
AbstractMicrogravity 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
Indexed BySCI ; EI
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6196
Collection空间技术部
Corresponding AuthorYu, Qiang (yuqiang@nssc.ac.cn)
Recommended Citation
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.
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