Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction
In this paper, a Wiener filtering algorithm in the wavelet domain is proposed to filter the laser self-mixing interference (SMI) signals, which is used to improve the accuracy of displacement reconstruction. The Wiener filter is theoretically constructed and applied to filter both high-frequency coe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/1/40 |
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| author | Hui Liu Xintao Li Yaqiang You Xia Liu Xiaohui Zhao Jian Sun Jingwei Wang Dong Hou |
| author_facet | Hui Liu Xintao Li Yaqiang You Xia Liu Xiaohui Zhao Jian Sun Jingwei Wang Dong Hou |
| author_sort | Hui Liu |
| collection | DOAJ |
| description | In this paper, a Wiener filtering algorithm in the wavelet domain is proposed to filter the laser self-mixing interference (SMI) signals, which is used to improve the accuracy of displacement reconstruction. The Wiener filter is theoretically constructed and applied to filter both high-frequency coefficients and low-frequency coefficients in the wavelet domain, which are obtained by two-level discrete wavelet transformation (DWT) decomposition from unfiltered SMI signals. Two-level wavelet decomposition in wavelet threshold filtering is determined without any manual judgment. Subsequently, the inverse DWT is employed to generate the filtered SMI signals. Compared with that, using wavelet threshold denoising, the results of the simulation and experiments demonstrate that the displacement reconstruction from the filtered SMI signals exhibits better accuracy when using Wiener filtering in the wavelet domain with two levels of wavelet decomposition. Also, the fake peaks due to local oscillation caused by wavelet threshold filtering can be eliminated effectively. The proposed method employs two-level wavelet decomposition, ensuring computational efficiency and achieving an 11.3% improvement in displacement reconstruction accuracy compared to wavelet threshold filtering. The maximum error ratio of the micro-displacement reconstruction is reduced to 2.7% using the Wiener filter in the wavelet domain. |
| format | Article |
| id | doaj-art-20033b37209f41f4afd34d51cd975762 |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-20033b37209f41f4afd34d51cd9757622025-01-24T13:46:17ZengMDPI AGPhotonics2304-67322025-01-011214010.3390/photonics12010040Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement ReconstructionHui Liu0Xintao Li1Yaqiang You2Xia Liu3Xiaohui Zhao4Jian Sun5Jingwei Wang6Dong Hou7School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710600, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710600, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710600, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710600, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710600, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710600, ChinaFocuslight Technologies Inc., Xi’an 710077, ChinaFocuslight Technologies Inc., Xi’an 710077, ChinaIn this paper, a Wiener filtering algorithm in the wavelet domain is proposed to filter the laser self-mixing interference (SMI) signals, which is used to improve the accuracy of displacement reconstruction. The Wiener filter is theoretically constructed and applied to filter both high-frequency coefficients and low-frequency coefficients in the wavelet domain, which are obtained by two-level discrete wavelet transformation (DWT) decomposition from unfiltered SMI signals. Two-level wavelet decomposition in wavelet threshold filtering is determined without any manual judgment. Subsequently, the inverse DWT is employed to generate the filtered SMI signals. Compared with that, using wavelet threshold denoising, the results of the simulation and experiments demonstrate that the displacement reconstruction from the filtered SMI signals exhibits better accuracy when using Wiener filtering in the wavelet domain with two levels of wavelet decomposition. Also, the fake peaks due to local oscillation caused by wavelet threshold filtering can be eliminated effectively. The proposed method employs two-level wavelet decomposition, ensuring computational efficiency and achieving an 11.3% improvement in displacement reconstruction accuracy compared to wavelet threshold filtering. The maximum error ratio of the micro-displacement reconstruction is reduced to 2.7% using the Wiener filter in the wavelet domain.https://www.mdpi.com/2304-6732/12/1/40self-mixing interferencedisplacement reconstructionwavelet domainWiener filteringphase unwrapping |
| spellingShingle | Hui Liu Xintao Li Yaqiang You Xia Liu Xiaohui Zhao Jian Sun Jingwei Wang Dong Hou Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction Photonics self-mixing interference displacement reconstruction wavelet domain Wiener filtering phase unwrapping |
| title | Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction |
| title_full | Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction |
| title_fullStr | Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction |
| title_full_unstemmed | Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction |
| title_short | Wiener Filtering in Wavelet Domain on Laser Self-Mixing Interference for Micro-Displacement Reconstruction |
| title_sort | wiener filtering in wavelet domain on laser self mixing interference for micro displacement reconstruction |
| topic | self-mixing interference displacement reconstruction wavelet domain Wiener filtering phase unwrapping |
| url | https://www.mdpi.com/2304-6732/12/1/40 |
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