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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

文献类型：期刊论文

作者Shang, Zhen-Hong3,4; Chen, Long4; Qiang, Zhen-Ping2; Bi Y(毕以)1; Li, Run-Xin3,4
刊名RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期2025-03-01
卷号25期号:3
关键词methods: data analysis techniques: image processing Sun: fundamental parameters
ISSN号1674-4527
DOI10.1088/1674-4527/adbc38
产权排序第4完成单位
文献子类Article
英文摘要Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics. This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field, addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images. The proposed method converts the transverse velocity field computation problem into an optical flow computation problem, using two forward propagations of features to get rid of the reliance on optical flow labels. Additionally, it reduces the impact of the Brightness Consistency constraint on optical flow accuracy by identifying and handling optical flow outliers. We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the H alpha and TiO bands, observed by the New Vacuum Solar Telescope. Comparative experiments with several well-established optical flow methods, including those based on supervised deep learning models, show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean, Residual Map Variance, Cross Correlation, and Structural Similarity Index Measure. Moreover, since optical flow captures the fundamental motion information in image sequences, the proposed method can be applied to a variety of research areas, including solar image registration, sequence alignment, image super-resolution, magnetic field calibration, and solar activity forecasting. The code is available at https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.
学科主题天文学 ; 太阳与太阳系
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词OPTICAL-FLOW
资助项目National Natural Science Foundation of China (NSFC)[12063002]; National Natural Science Foundation of China (NSFC)[12163004]
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001451116500001
出版者IOP Publishing Ltd
资助机构National Natural Science Foundation of China (NSFC)[12063002, 12163004]
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/28234]  
专题云南天文台_其他
作者单位1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China
2.College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China;
3.Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China;
4.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;
推荐引用方式
GB/T 7714		 Shang, Zhen-Hong,Chen, Long,Qiang, Zhen-Ping,et al. Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2025,25(3).
APA Shang, Zhen-Hong,Chen, Long,Qiang, Zhen-Ping,毕以,&Li, Run-Xin.(2025).Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,25(3).
MLA Shang, Zhen-Hong,et al."Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 25.3(2025).

入库方式： OAI收割

来源：云南天文台

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