Abstract
In this paper, an improved method of measuring wavefront aberration based on image with machine learning is proposed. This method had better real-time performance and higher estimation accuracy in free space optical communication in cases of strong atmospheric turbulence. We demonstrated that the network we optimized could use the point spread functions (PSFs) at a defocused plane to calculate the corresponding Zernike coefficients accurately. The computation time of the network was about 6–7 ms and the root-mean-square (RMS) wavefront error (WFE) between reconstruction and input was, on average, within 0.1263 waves in the situation of D/r0 = 20 in simulation, where D was the telescope diameter and r0 was the atmospheric coherent length. Adequate simulations and experiments were carried out to indicate the effectiveness and accuracy of the proposed method.
Highlights
Wavefront aberrations generated by atmospheric turbulence affect the distribution of focus, deteriorate the fiber coupling efficiency and the quality of communication
Measurement of wavefront aberration in free space optical communication differs from other scenarios in that the atmospheric turbulence changes constantly
We demonstrated that the trained network could be used to calculate Zernike coefficients without nonlinear optimization for the case of strong atmospheric turbulence
Summary
Wavefront aberrations generated by atmospheric turbulence affect the distribution of focus, deteriorate the fiber coupling efficiency and the quality of communication. High-accuracy correction of wavefront aberration requires accurate real-time measurement in free space optical communication. Inception V3 [16], a convolutional neural network that performs well in image classification, was used to measure wavefront aberration in Reference [17]. The output of the network was, on average, within 0.37 waves RMS wavefront error (WFE) of the true solution Using these initial estimations as the starting value of the nonlinear optimization, the error was reduced to within 0.1 waves. In order to advance the real-time performance and fitting accuracy of measuring wavefront aberration with a CNN, an improved method is proposed in this paper. We demonstrated that the trained network could be used to calculate Zernike coefficients without nonlinear optimization for the case of strong atmospheric turbulence.
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