计算机科学
干涉测量
计量学
相(物质)
人工智能
相位噪声
噪音(视频)
分割
光学
相位恢复
散斑噪声
模棱两可
计算机视觉
斑点图案
数学
物理
图像(数学)
傅里叶变换
数学分析
程序设计语言
量子力学
作者
Junchao Zhang,Xiaobo Tian,Jianbo Shao,Haibo Luo,Rongguang Liang
出处
期刊:Optics Express
[The Optical Society]
日期:2019-05-08
卷期号:27 (10): 14903-14903
被引量:92
摘要
The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.
科研通智能强力驱动
Strongly Powered by AbleSci AI