Wavelet and CNN-Based Denoising of Digital Coherent Nanoscale Displacement Sensing

A phase signal denoising method based on wavelet transformation and CNN self-supervised learning is proposed to denoise nanoscale displacement signals. The spectrum smoothness and measurement accuracy are effectively improved.

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Publication Details

Published
2025-06-29
DOI
https://doi.org/10.23919/oecc/psc62146.2025.11109545
Primary Topic
Advanced MEMS and NEMS Technologies
Type
article
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article

Wavelet and CNN-Based Denoising of Digital Coherent Nanoscale Displacement Sensing

Tianxiang Yuan, Li Zhu, Masanori Hanawa, Ayumi Ito
Advanced MEMS and NEMS Technologies
article

Wavelet and CNN-Based Denoising of Digital Coherent Nanoscale Displacement Sensing

Tianxiang Yuan, Li Zhu, Masanori Hanawa, Ayumi Ito
article en

Abstract

A phase signal denoising method based on wavelet transformation and CNN self-supervised learning is proposed to denoise nanoscale displacement signals. The spectrum smoothness and measurement accuracy are effectively improved.

Hangzhou Dianzi University (CN), University of Yamanashi (JP)
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Wavelet and CNN-Based Denoising of Digital Coherent Nanoscale Displacement Sensing — Tianxiang Yuan, Li Zhu, et al. · (2025) | TGRS Research Map | TGRS