Spherical aberration coefficients identification of spherical aberration interferograms based on deep learning algorithm

Yingjie Yu; Jiaxing LI

doi:10.1117/12.2501004

24 July 2018 Spherical aberration coefficients identification of spherical aberration interferograms based on deep learning algorithm

Yingjie Yu, Jiaxing LI

Author Affiliations +

Proceedings Volume 10827, Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018); 108271K (2018) https://doi.org/10.1117/12.2501004
Event: Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018), 2018, Shanghai, China

Abstract

In the field of optical measurement,the spherical aberration always causes a circular speckle which is symmetrical to the optic axis on interferograms and it can even blur the images.In this paper, a deep learning algorithm is put forward to identify the spherical aberration coefficients of monochromatic spherical aberration interferograms.The deep learning algorithm needs sufficient training samples, but it also takes a lot of time to collect the real interferograms as the training samples.The method of zernike polynomial fitting wave surface is much better relative to the least square method.Therefore,this paper adopts the zernike polynomials fitting method,by means of changing aberration coefficients,to obtain a large number of training samples of spherical aberration interferograms by computer simulation.Different from other traditional methods, using deep learning algorithms to identify the information of the interferograms is more efficient, the spherical aberration coefficients of interferograms can be accurately and quickly identified.What is more,this method also simplify the manual processing.

Citation Download Citation

Yingjie Yu and Jiaxing LI "Spherical aberration coefficients identification of spherical aberration interferograms based on deep learning algorithm", Proc. SPIE 10827, Sixth International Conference on Optical and Photonic Engineering (icOPEN 2018), 108271K (24 July 2018); https://doi.org/10.1117/12.2501004

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KEYWORDS

Monochromatic aberrations

Neural networks

Wavefronts

Zernike polynomials

Computer simulations

Data modeling

Interferometry

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