Zhang Tianshan, Lu Ming-Feng, Wu Jin-Min, He Wenjie, Zhang Feng, Tao Ran
Appl Opt. 2024 Mar 1;63(7):1854-1866. doi: 10.1364/AO.511173.
As a typical form of optical fringes with a quadratic phase, Newton's ring patterns play an important role in spherical measurements and optical interferometry. A variety of methods have been used to analyze Newton's ring patterns. However, it is still rather challenging to fulfill the analysis. We present a deep-learning-based method to estimate the parameters of Newton's ring patterns and fulfill the analysis accordingly. The experimental results indicate the excellent accuracy, noise robustness, and demodulation efficiency of our method. It provides another applicable approach to analyzing Newton's ring patterns and brings insights into fringe analysis and interferometry-based measurements.
作为具有二次相位的典型光学条纹形式，牛顿环图案在球面测量和光学干涉测量中起着重要作用。人们已经使用了多种方法来分析牛顿环图案。然而，完成这种分析仍然颇具挑战性。我们提出了一种基于深度学习的方法来估计牛顿环图案的参数，并据此完成分析。实验结果表明了我们方法具有出色的准确性、噪声鲁棒性和解调效率。它为分析牛顿环图案提供了另一种适用方法，并为条纹分析和基于干涉测量的测量带来了新的见解。
