arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.13400physics.opticscs.LGeess.IV
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基于微分模型的透射和反射光自适应光学

Differentiable model-based adaptive optics with transmitted and reflected light

Ivan Vishniakou, Johannes D. Seelig

摘要:

像差在许多情况下限制光学系统,例如当在生物组织中成像时。机器学习通过学习像差的逆模型提供了在这种条件下改善成像的新方法。学习需要覆盖广泛的可能的像差的数据集,然而,这对于更强散射的样本来说是有限的,并且没有利用关于成像过程的先验信息。在这里,我们展示了将基于模型的自适应光学与机器学习框架的优化技术相结合,可以通过少量的测量找到像差校正。在透射配置中通过单个像差层和在反射配置中同时通过两个不同的层来确定校正。另外,校正不受像差的预定模型(诸如Zernike模式的组合)的限制。透射中的聚焦可以仅基于反射光来实现,与落射探测成像配置兼容。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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