论文标题
带有传输和反射光的基于模型的自适应光学器件
Differentiable model-based adaptive optics with transmitted and reflected light
论文作者
论文摘要
畸变限制了许多情况下的光学系统，例如在生物组织中成像时。机器学习提供了通过学习畸变的逆模型在这种情况下改善成像的新颖方法。学习需要涵盖广泛可能的畸变的数据集，但是对于更强烈的散射样本而言，这会限制，并且不利用有关成像过程的先前信息。在这里，我们表明，将基于模型的自适应光学器件与机器学习框架的优化技术相结合可以通过少量测量找到像差校正。通过单个像差层和通过两个不同层的反射配置在传输配置中确定校正。另外，校正不受预定的畸变模型（例如Zernike模式的组合）的限制。只需基于反射光即可实现关注变速箱，与附表成像配置兼容。
Aberrations limit optical systems in many situations, for example when imaging in biological tissue. Machine learning offers novel ways to improve imaging under such conditions by learning inverse models of aberrations. Learning requires datasets that cover a wide range of possible aberrations, which however becomes limiting for more strongly scattering samples, and does not take advantage of prior information about the imaging process. Here, we show that combining model-based adaptive optics with the optimization techniques of machine learning frameworks can find aberration corrections with a small number of measurements. Corrections are determined in a transmission configuration through a single aberrating layer and in a reflection configuration through two different layers at the same time. Additionally, corrections are not limited by a predetermined model of aberrations (such as combinations of Zernike modes). Focusing in transmission can be achieved based only on reflected light, compatible with an epidetection imaging configuration.
