Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns
Fringe patterns are often produced from optical metrology. It is important yet challenging to reduce noise and remove a complicated background in a fringe pattern, for which empirical mode decomposition based methods have been proven useful. However, the mode-mixing problem and the difficulty in aut...
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sg-ntu-dr.10356-818012020-05-28T07:18:47Z Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns Wang, Chenxing Qian, Kemao Da, Feipeng School of Computer Engineering Multi-plAtform Game Innovation Centre Image analysis Phase retrieval Fringe patterns are often produced from optical metrology. It is important yet challenging to reduce noise and remove a complicated background in a fringe pattern, for which empirical mode decomposition based methods have been proven useful. However, the mode-mixing problem and the difficulty in automatic mode classification limit the application of these methods. In this paper, a newly developed method named regenerated phase-shifted sinusoids assisted empirical mode decomposition is introduced to decompose a fringe pattern, and subsequently, a new noise-signal-background classification strategy is proposed. The former avoids the mode-mixing problem appearing during the decomposition, while the latter adaptively classifies the decomposition results to remove the noise and background. The proposed method is testified by both simulation and real experiments, which shows effective and robust for fringe pattern analysis under different noise, fringe modulation, and defects. NRF (Natl Research Foundation, S’pore) 2016-07-29T08:23:45Z 2019-12-06T14:40:45Z 2016-07-29T08:23:45Z 2019-12-06T14:40:45Z 2016 2016 Journal Article Wang, C., Qian, K., & Da, F. (2016). Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns. Optics and Lasers in Engineering, in press. 0143-8166 https://hdl.handle.net/10356/81801 http://hdl.handle.net/10220/41029 10.1016/j.optlaseng.2016.04.018 192744 en Optics and Lasers in Engineering © 2016 Elsevier. |
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Image analysis Phase retrieval Wang, Chenxing Qian, Kemao Da, Feipeng Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns |
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Fringe patterns are often produced from optical metrology. It is important yet challenging to reduce noise and remove a complicated background in a fringe pattern, for which empirical mode decomposition based methods have been proven useful. However, the mode-mixing problem and the difficulty in automatic mode classification limit the application of these methods. In this paper, a newly developed method named regenerated phase-shifted sinusoids assisted empirical mode decomposition is introduced to decompose a fringe pattern, and subsequently, a new noise-signal-background classification strategy is proposed. The former avoids the mode-mixing problem appearing during the decomposition, while the latter adaptively classifies the decomposition results to remove the noise and background. The proposed method is testified by both simulation and real experiments, which shows effective and robust for fringe pattern analysis under different noise, fringe modulation, and defects. |
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School of Computer Engineering |
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School of Computer Engineering Wang, Chenxing Qian, Kemao Da, Feipeng |
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Article |
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Wang, Chenxing Qian, Kemao Da, Feipeng |
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Wang, Chenxing |
title |
Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns |
title_short |
Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns |
title_full |
Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns |
title_fullStr |
Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns |
title_full_unstemmed |
Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns |
title_sort |
regenerated phase-shifted sinusoids assisted emd for adaptive analysis of fringe patterns |
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2016 |
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https://hdl.handle.net/10356/81801 http://hdl.handle.net/10220/41029 |
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1681058022340689920 |