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Motion parameters estimation based on improved radon transform for blurred star image

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Motion parameters estimation is a crucial key to blur star restoration which is helpful to improve dynamic performance of star sensor. However, motion blur under high dynamic conditions results in signal-to-noise ratio (SNR) decreasing seriously which brings about difficulties in motion parameters estimation. In our paper, an improved Radon transform by introducing a combination of Z-function and double threshold mask is proposed overcoming the obstacles introduced by noise. The proposed algorithm is effectively in enhancing parallel stripes on Fourier spectrum and can reduce estimation error introduced by noise. Moreover, motion parameters can be obtained accurately even under conditions with SNR as low as 9.1629 dB by applying our algorithm. Finally, experiments on simulated images as well as real ones with different motion angles and motion lengths are carried out, testifying the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationIST 2016 - 2016 IEEE International Conference on Imaging Systems and Techniques, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-47
Number of pages6
ISBN (Electronic)9781509018178
DOIs
StatePublished - 7 Nov 2016
Event2016 IEEE International Conference on Imaging Systems and Techniques, IST 2016 - Chania, Crete Island, Greece
Duration: 4 Oct 20166 Oct 2016

Publication series

NameIST 2016 - 2016 IEEE International Conference on Imaging Systems and Techniques, Proceedings

Conference

Conference2016 IEEE International Conference on Imaging Systems and Techniques, IST 2016
Country/TerritoryGreece
CityChania, Crete Island
Period4/10/166/10/16

Keywords

  • blurred star image
  • mask
  • motion parameters estimation
  • nonlinear gray stretching
  • Radon transform

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