Measurement of ocular aberration in noise based on deep learning with a Shack-Hartmann wavefront sensor

Shack-Hartmann-based wavefront sensing combined with deep learning, due to its fast, accurate, and large dynamic range, has been widely studied in many fields including ocular aberration measurement. Problems such as noise and corneal reflection affect the accuracy of detection in practical measuring ocular aberration systems. This paper establishes a framework comprising of a noise-added model, Hartmannograms with corneal reflections and the corneal reflection elimination algorithm. Therefore, a more realistic data set is obtained, enabling the convolutional neural network to learn more comprehensive features and carry out real machine verification. The results show that the proposed method has excellent measurement accuracy. The root mean square error (RMSE) of the residual wavefront is 0.00924 ± 0.0207λ (mean ± standard deviation) in simulation and 0.0496 ± 0.0156λ in a real machine. Compared with other methods, this network combined with the proposed corneal reflection elimination algorithm is more accurate, speedier, and more widely applicable in the noise and corneal reflection situations, making it a promising tool for ocular aberration measurement.