Imaging with phase diversity: simulations with a neural network
Imaging with phase diversity: simulations with a neural network
复制标题
相位多样性成像：神经网络模拟
DOI：
10.1117/12.142052
复制
发表时间：
1993
期刊：
影响因子：
--
通讯作者：
A. Ling
中科院分区：
文献类型：
--
作者：
Nancy A. Miller;A. Ling
提出了一种利用相位差估计未知扩展目标像差的方法。由于非线性优化算法，相位分集的原始版本需要大量的处理，这在实时系统中是禁止的。因此，神经网络被探索作为一种替代的解决方案的问题，本文显示了传统的相位分集方法的修改，采用神经网络来估计点源和扩展场景数据的像差。模拟表明，像差可以估计为0.02波均方根的平均误差。
The technique of phase diversity was proposed for estimating telescope aberrations for an unknown extended object. The original version of phase diversity requires extensive processing due to a nonlinear optimization algorithm which is prohibitive in a real-time system. Therefore, neural networks were explored as an alternative solution of the problem and this paper shows the modification of the traditional phase diversity method to employ neural networks to estimate aberrations of point source and extended scene data. Simulations indicated aberrations could be estimated to an average error of 0.02 waves rms.