scholarly journals A Study on atmospheric turbulence with Shearing Interferometer wavefront sensor

2015 ◽  
Vol 04 (03) ◽  
pp. 991-999
Author(s):  
M.Mohamed Ismail, M.Mohamed Sathik
Keyword(s):  
Atmospheric Turbulence ◽  
Wavefront Sensor

Measurements of Atmospheric Turbulence from Image Motion of Laser Beam by Shack-Hartmann Wavefront Sensor

2019 ◽  
Author(s):  
Lidiia Bolbasova ◽  
Alexey Gritsuta ◽  
Vitaliy Lavrinov ◽  
Vladimir Lukin ◽  
Anton Selin ◽  
...  
Keyword(s):  
Laser Beam ◽  
Atmospheric Turbulence ◽  
Image Motion ◽  
Wavefront Sensor

scholarly journals Estimation of atmospheric turbulence parameters from Shack–Hartmann wavefront sensor measurements

2018 ◽  
Vol 483 (1) ◽  
pp. 1192-1201 ◽  
Author(s):  
Paulo P Andrade ◽  
Paulo J V Garcia ◽  
Carlos M Correia ◽  
Johann Kolb ◽  
Maria Inês Carvalho
Keyword(s):  
Atmospheric Turbulence ◽  
Wavefront Sensor ◽  
Turbulence Parameters

Study of atmospheric turbulence with Shack Hartmann wavefront sensor

2013 ◽  
Vol 42 (2) ◽  
pp. 128-140 ◽  
Author(s):  
Narsireddy Anugu ◽  
J. P. Lancelot
Keyword(s):  
Atmospheric Turbulence ◽  
Wavefront Sensor

Atmospheric turbulence profiling with a Shack-Hartmann wavefront sensor

2020 ◽  
Author(s):  
Hajime Ogane ◽  
Masayuki Akiyama ◽  
Shin Oya ◽  
Yoshito H. Ono
Keyword(s):  
Atmospheric Turbulence ◽  
Wavefront Sensor

Atmospheric turbulence meter based on a Shack–Hartmann wavefront sensor

2019 ◽  
Vol 86 (7) ◽  
pp. 426 ◽  
Author(s):  
L. A. Bolbasova ◽  
A. N. Gritsuta ◽  
E. A. Kopylov ◽  
V. V. Lavrinov ◽  
V. P. Lukin ◽  
...  
Keyword(s):  
Atmospheric Turbulence ◽  
Wavefront Sensor

scholarly journals Deep learning wavefront sensing and aberration correction in atmospheric turbulence

PhotoniX ◽  
2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Kaiqiang Wang ◽  
MengMeng Zhang ◽  
Ju Tang ◽  
Lingke Wang ◽  
Liusen Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Wavefront Sensor ◽  
Wavefront Aberration ◽  
Wavefront Sensing ◽  
Aberration Correction ◽  
Single Object ◽  
Multiple Objects ◽  
Correction Effect ◽  
And Performance

AbstractDeep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways: (i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What’s more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.

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