Appl Opt. 2021 Dec 1;60(34):10671-10679. doi: 10.1364/AO.443107.
The measurement of the diopters of spectacle lenses using an artificial neural network is proposed in this paper. A diopter is measured by obtaining the distances between the spots imaged by a charge-coupled device (CCD) during a Hartmann test. Backpropagation (BP) and a radial basis function (RBF) algorithm are applied to train the BP and RBF neural networks, respectively. A set of -20 to 20 spectacle lenses is used to test the proposed method. When comparing the diopter measurement results of the RBF neural network with those of the BP neural network, the former exhibited higher stability and lower errors. In addition, the errors of diopter measurement are not against the system error measured by an auto-focimeter due to the increase of curvature of spectacle lenses. These results indicated that RBF neural network has better performance in diopter detection, and it can overcome the system error caused by the change of lens curvature in auto-focimeter measurement.
本文提出了一种利用人工神经网络测量眼镜镜片屈光度的方法。屈光度的测量是通过获取在哈特曼测试中 CCD 成像的光斑之间的距离来实现的。反向传播（BP）和径向基函数（RBF）算法分别应用于训练 BP 和 RBF 神经网络。使用一组-20 到 20 的眼镜镜片来测试所提出的方法。当将 RBF 神经网络的屈光度测量结果与 BP 神经网络的结果进行比较时，前者表现出更高的稳定性和更低的误差。此外，由于眼镜曲率的增加，屈光度测量的误差不会对自动对焦仪测量的系统误差产生影响。这些结果表明，RBF 神经网络在屈光度检测方面具有更好的性能，它可以克服自动对焦仪测量中由于镜片曲率变化引起的系统误差。
