Optical microscopes have long been essential for many scientific disciplines. However, the resolution and contrast of such microscopic images are dramatically affected by aberrations. In this study, compacted with adaptive optics, we propose a machine learning technique, called the ‘phase-retrieval deep convolutional neural networks (PRDCNNs)’. This aberration determination architecture is direct and exhibits high accuracy and certain generalisation ability. Notably, its performance surpasses those of similar, existing methods, with fewer fluctuations and greater robustness against noise. We anticipate future application of the proposed PRDCNNs to super-resolution microscopes.

中文翻译：

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使用带自注意力的深度卷积神经网络进行高精度、直接的像差确定

长期以来，光学显微镜对于许多科学学科来说都是必不可少的。然而，这种显微图像的分辨率和对比度会受到像差的显着影响。在这项研究中，结合自适应光学，我们提出了一种机器学习技术，称为“相位检索深度卷积神经网络 (PRDCNN)”。这种像差判定架构是直接的，具有较高的准确性和一定的泛化能力。值得注意的是，它的性能超过了类似的现有方法，波动更少，对噪声的鲁棒性更强。我们预计未来将提议的 PRDCNN 应用于超分辨率显微镜。