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

Bohang Zhong,

Huaian Yi, Seokyoung Ahn

и другие.

Опубликована: Дек. 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.

Язык: Английский

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

Jia Cong,

Yuxin Shen

и другие.

Optics & Laser Technology, Год журнала: 2025, Номер 190, С. 113185 - 113185

Опубликована: Май 19, 2025

Язык: Английский

Процитировано

0

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

Luo Guoping

и другие.

Optics and Lasers in Engineering, Год журнала: 2024, Номер 176, С. 108114 - 108114

Опубликована: Фев. 14, 2024

Язык: Английский

Процитировано

0

Phase unwrapping via fully exploiting global and local spatial dependencies DOI

Yuhui Quan,

Xin Yao, Zhifeng Chen

и другие.

Optics & Laser Technology, Год журнала: 2024, Номер 181, С. 111872 - 111872

Опубликована: Окт. 2, 2024

Язык: Английский

Процитировано

0

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

Bohang Zhong,

Huaian Yi, Seokyoung Ahn

и другие.

Опубликована: Дек. 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.

Язык: Английский

Процитировано

0