In:
Applied Optics, Optica Publishing Group, Vol. 60, No. 13 ( 2021-05-01), p. 3964-
Abstract:
By analyzing Newton’s rings, often encountered in interferometry, the parameters of spherical surfaces such as the rings’ center and the curvature radius can be estimated. First, the classical convolutional neural networks, visual geometry group (VGG) network and U-Net, are applied to parameter estimation of Newton’s rings. After these models are trained, the rings’ center and curvature radius can be obtained simultaneously. Compared with previous analysis methods of Newton’s rings, it is shown that the proposed method has higher precision, better immunity to noise, and lower time consumption. For a Newton’s rings pattern of 640 × 480 pixels comprising − 5 d B Gaussian noise or 60% salt-and-pepper noise, the parameters can be estimated by the VGG model in 0.01 s, the error of the rings’ center is less than one pixel, and the error of curvature radius is lower than 0.5%.
Type of Medium:
Online Resource
ISSN:
1559-128X
,
2155-3165
Language:
English
Publisher:
Optica Publishing Group
Publication Date:
2021
detail.hit.zdb_id:
207387-0
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