Shack-Hartmann spot dislocation map determination using an optical flow method

Large-Dynamic-Range Ocular Aberration Measurement Based on Deep Learning with a Shack-Hartmann Wavefront Sensor

The Shack-Hartmann wavefront sensor (SHWFS) is widely utilized for ocular aberration measurement. However, large ocular aberrations caused by individual differences can easily make the spot move out of the range of the corresponding sub-aperture in SHWFS, rendering the traditional centroiding method ineffective. This study applied a novel convolutional neural network (CNN) model to wavefront sensing for large dynamic ocular aberration measurement. The simulation results demonstrate that, compared to the modal method, the dynamic range of our method for main low-order aberrations in ocular system is increased by 1.86 to 43.88 times in variety. Meanwhile, the proposed method also has the best measurement accuracy, and the statistical root mean square (RMS) of the residual wavefronts is 0.0082 ± 0.0185 λ (mean ± standard deviation). The proposed method generally has a higher accuracy while having a similar or even better dynamic range as compared to traditional large-dynamic schemes. On the other hand, compared with recently developed deep learning methods, the proposed method has a much larger dynamic range and better measurement accuracy.

Ocular aberration measurement with and without an aperture stop using a Shack-Hartmann wavefront sensor

Pupil size is an important parameter since it governs the magnitude of ocular aberrations. The pupil size of a human eye has significant individual differences and varies with light level and accommodation. In order to accurately measure ocular aberrations under different pupil sizes using a Shack-Hartmann wavefront sensor (SHWFS), two types of relationship matrices R (1) and R (2) were proposed, which corresponded to wavefront reconstruction with and without an aperture stop, respectively. The numerical and experimental results indicated that matrix R (2) can significantly improve the accuracy of wavefront restoration when the incident beam size is inconsistent with the wavefront reconstruction aperture. Meanwhile, the impact of the aperture stop on the reconstruction accuracy will become smaller and smaller as the ratio ρ of the outer area to the detection aperture decreases. This study not only can be used for accurately measuring ocular aberrations under different pupil sizes, but also for other variable aperture aberrations measurement in other applications.

A Method Used to Improve the Dynamic Range of Shack-Hartmann Wavefront Sensor in Presence of Large Aberration

With the successful application of the Shack-Hartmann wavefront sensor in measuring aberrations of the human eye, researchers found that, when the aberration is large, the local wavefront distortion is large, and it causes the spot corresponding to the sub-aperture of the microlens to shift out of the corresponding range of the sub-aperture. However, the traditional wavefront reconstruction algorithm searches for the spot within the corresponding range of the sub-aperture of the microlens and reconstructs the wavefront according to the calculated centroid, which leads to wavefront reconstruction errors. To solve the problem of the small dynamic range of the Shack-Hartmann wavefront sensor, this paper proposes a wavefront reconstruction algorithm based on the autocorrelation method and a neural network. The autocorrelation centroid extraction method was used to calculate the centroid in the entire spot map in order to obtain a centroid map and to reconstruct the wavefront by matching the centroid with the microlens array through the neural network. This method breaks the limitation of the sub-aperture of the microlens. The experimental results show that the algorithm improves the dynamic range of the first 15 terms of the Zernike aberration reconstruction to varying degrees, ranging from 62.86% to 183.87%.

Centroid computation for Shack-Hartmann wavefront sensor in extreme situations based on artificial neural networks

This paper proposes a method used to calculate centroid for Shack-Hartmann wavefront sensor (SHWFS) in adaptive optics (AO) systems that suffer from strong environmental light and noise pollutions. In these extreme situations, traditional centroid calculation methods are invalid. The proposed method is based on the artificial neural networks that are designed for SHWFS, which is named SHWFS-Neural Network (SHNN). By transforming spot detection problem into a classification problem, SHNNs first find out the spot center, and then calculate centroid. In extreme low signal-noise ratio (SNR) situations with peak SNR (SNR_p) of 3, False Rate of SHNN-50 (SHNN with 50 hidden layer neurons) is 6%, and that of SHNN-900 (SHNN with 900 hidden layer neurons) is 0%, while traditional methods' best result is 26 percent. With the increase of environmental light interference's power, the False Rate of SHNN-900 remains around 0%, while traditional methods' performance decreases dramatically. In addition, experiment results of the wavefront reconstruction are presented. The proposed SHNNs achieve significantly improved performance, compared with the traditional method, the Root Mean Square (RMS) of residual decreases from 0.5349 um to 0.0383 um. This method can improve SHWFS's robustness.