Long Exposure Point Spread Function Modeling with Gaussian Processes

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• 关键词：Long exposure ; Modeling PSF ; Gaussian process
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9719
• 期：1
• 页码：540-546
• 全文大小：1,076 KB
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• 作者单位：Ping Guo (16)
  Jian Yu (16)
  Qian Yin (16)
  
  16. Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, 100875, China
  
• 丛书名：Advances in Neural Networks ¨C ISNN 2016
• ISBN：978-3-319-40663-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9719

文摘

The long exposure point spread function (PSF) model is commonly used to improve signal-noise-ratio of astronomical object imaging and reduce the effect of atmospheric turbulence. In this paper, a move-and-superposition method of modeling the long exposure PSF based on Gaussian process is proposed. Experimental results show that the proposed modeling method can obtain more accurate estimation of final PSF for astronomical object imaging process than that of the simple shift-and-add PSF model.