Super resolution for astronomical observations

  • Zhan Li
  • Qingyu Peng
  • Bir Bhanu
  • Qingfeng Zhang
  • Haifeng He
Original Article


In order to obtain detailed information from multiple telescope observations a general blind super-resolution (SR) reconstruction approach for astronomical images is proposed in this paper. A pixel-reliability-based SR reconstruction algorithm is described and implemented, where the developed process incorporates flat field correction, automatic star searching and centering, iterative star matching, and sub-pixel image registration. Images captured by the 1-m telescope at Yunnan Observatory are used to test the proposed technique. The results of these experiments indicate that, following SR reconstruction, faint stars are more distinct, bright stars have sharper profiles, and the backgrounds have higher details; thus these results benefit from the high-precision star centering and image registration provided by the developed method. Application of the proposed approach not only provides more opportunities for new discoveries from astronomical image sequences, but will also contribute to enhancing the capabilities of most spatial or ground-based telescopes.


Methods: data analysis Techniques: image processing Telescopes Stars: imaging 



The authors would like to thank the staff working with the 1-m telescope at Yunnan Observatory for their kind help with the collection of the observation data used in this paper. The research is financially supported by the National Natural Science Foundation of China (Grant No. 11403008, U1431227, 11273014), Natural Science Foundation of Guangdong (Grant No. 2014A030313374, 2016A030313092), and the Fundamental Research Funds for the Central Universities.


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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceJinan UniversityGuangzhouChina
  2. 2.Sino-France Joint Laboratory for Astrometry, Dynamics and Space ScienceJinan UniversityGuangzhouChina
  3. 3.Center for Research in Intelligent Systems, Department of Electrical and Computer EngineeringUniversity of CaliforniaRiversideUSA

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