波前
光学
人工神经网络
计算机科学
特征(语言学)
无监督学习
模式识别(心理学)
自适应光学
特征提取
变量(数学)
对象(语法)
人工智能
计算机视觉
物理
数学
语言学
哲学
数学分析
作者
Xinlan Ge,Licheng Zhu,Zeyu Gao,Ning Wang,Hongwei Ye,Shuai Wang,Ping Yang
出处
期刊:Optics Letters
[The Optical Society]
日期:2023-08-02
卷期号:48 (17): 4476-4476
被引量:2
摘要
This Letter introduces the idea of unsupervised learning into object-independent wavefront sensing for the first time, to the best of our knowledge, which can achieve fast phase recovery of arbitrary objects without labels. First, a fine feature extraction method which only depends on the wavefront aberrations is proposed. Then, a lightweight neural network and an optical feature system are combined to form an unsupervised learning model, and the neural network is promoted to be well trained by reversely outputting fine features. Simulation results prove that the proposed method can effectively overcome the aberrations (static or variable) existing in the optical system and achieve wavefront sensing of different objects with high precision and efficiency.
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