Marginal estimation of aberrations and image restoration by use of phase diversity.
Marginal estimation of aberrations and image restoration by use of phase diversity.
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DOI：
10.1364/josaa.20.001035
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发表时间：
2003-06
期刊：
影响因子：
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通讯作者：
A. Blanc;L. Mugnier;J. Idier
中科院分区：
文献类型：
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作者：
A. Blanc;L. Mugnier;J. Idier
我们提出了一种新的方法称为边缘估计估计的像差和对象的相位分集数据。传统的估计发现在文献中的技术首先提出的Gonsalves有其基础上的联合估计的畸变相位和观察到的对象。通过模拟，我们研究了传统的估计，这是一个联合的最大后验方法解释的行为，我们特别表明，它具有不良的渐近性质，不允许一个最佳的联合估计的对象和像差相位。我们提出了一种新的最大后验概率边缘估计的唯一相位。它是通过将观察到的对象从问题中积分出来得到的。这大大减少了未知数的数量，允许正则化参数的无监督估计，并提供更好的渐近性质。我们表明，边缘的方法也是适当的对象的恢复。实现了该估计器，并通过仿真验证了其性能。在对地观测的模拟数据和实验数据上比较了联合方法和边际方法的性能。对于所研究的对象，相位恢复质量的比较表明，在高噪声水平条件下，边缘方法的性能更好。
We propose a novel method called marginal estimator for estimating the aberrations and the object from phase-diversity data. The conventional estimator found in the literature concerning the technique first proposed by Gonsalves has its basis in a joint estimation of the aberrated phase and the observed object. By means of simulations, we study the behavior of the conventional estimator, which is interpretable as a joint maximum a posteriori approach, and we show in particular that it has undesirable asymptotic properties and does not permit an optimal joint estimation of the object and the aberrated phase. We propose a novel marginal estimator of the sole phase by maximum a posteriori. It is obtained by integrating the observed object out of the problem. This reduces drastically the number of unknowns, allows the unsupervised estimation of the regularization parameters, and provides better asymptotic properties. We show that the marginal method is also appropriate for the restoration of the object. This estimator is implemented and its properties are validated by simulations. The performance of the joint method and the marginal one is compared on both simulated and experimental data in the case of Earth observation. For the studied object, the comparison of the quality of the phase restoration shows that the performance of the marginal approach is better under high-noise-level conditions.