Phase unwrapping via deep learning for surface shape measurement by using wavelength tuning interferometry DOI

Bohang Zhong,

Huaian Yi, Seokyoung Ahn

et al.

Published: Dec. 30, 2023

Wavelength tuning interferometry is widely used in optical metrology order to obtain the phase information of sample. The obtained wrapped usually unwraps range [-π, π]. Therefore, unwrapping operation required right phase. But some factors, such as shift miscalibration, coupling error, and noise, always lower precision conventional shifting algorithm. To address kind problems, we proposed a deep learning method using convolutional neural network perform process by turning task into multiclass classification work 2N - 1 for generating training dataset. results indicated that not only can compensate miscalibration but also has strong robust denoise ability, which means outperformed other measurement algorithms.

Language: Английский

ATCM-Net: A deep learning method for phase unwrapping based on perception optimization and learning enhancement DOI
Min Xu,

Jia Cong,

Yuxin Shen

et al.

Optics & Laser Technology, Journal Year: 2025, Volume and Issue: 190, P. 113185 - 113185

Published: May 19, 2025

Language: Английский

Citations

0

A divided difference filter-based phase unwrapping method DOI
Xianming Xie, Rong Li,

Luo Guoping

et al.

Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 176, P. 108114 - 108114

Published: Feb. 14, 2024

Language: Английский

Citations

0

Phase unwrapping via fully exploiting global and local spatial dependencies DOI

Yuhui Quan,

Xin Yao, Zhifeng Chen

et al.

Optics & Laser Technology, Journal Year: 2024, Volume and Issue: 181, P. 111872 - 111872

Published: Oct. 2, 2024

Language: Английский

Citations

0

Phase unwrapping via deep learning for surface shape measurement by using wavelength tuning interferometry DOI

Bohang Zhong,

Huaian Yi, Seokyoung Ahn

et al.

Published: Dec. 30, 2023

Wavelength tuning interferometry is widely used in optical metrology order to obtain the phase information of sample. The obtained wrapped usually unwraps range [-π, π]. Therefore, unwrapping operation required right phase. But some factors, such as shift miscalibration, coupling error, and noise, always lower precision conventional shifting algorithm. To address kind problems, we proposed a deep learning method using convolutional neural network perform process by turning task into multiclass classification work 2N - 1 for generating training dataset. results indicated that not only can compensate miscalibration but also has strong robust denoise ability, which means outperformed other measurement algorithms.

Language: Английский

Citations

0