Maximum-likelihood refinement for coherent diffractive imaging
Maximum-likelihood refinement for coherent diffractive imaging
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DOI：
10.1088/1367-2630/14/6/063004
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发表时间：
2012-06-07
影响因子：
3.3
通讯作者：
Guizar-Sicairos, M.
中科院分区：
文献类型：
--
作者：
Thibault, P.;Guizar-Sicairos, M.
介绍了最大似然原理在相干衍射成像中的应用。我们描述了一个实现的优化过程重叠，使用共轭梯度，包括预处理策略，正则化和典型的修改的统计噪声模型。的优化原理进行比较的差异图重建算法。与模拟数据的重要改进，观察到的信号噪声比的大幅增加。分辨率和灵敏度的显着收益也表现在ML细化的实验X射线数据的重建。我们的研究结果的直接后果是可能减少曝光，或剂量，由一个数量级的重建质量类似于目前使用的迭代算法。
We introduce the application of maximum-likelihood (ML) principles to the image reconstruction problem in coherent diffractive imaging. We describe an implementation of the optimization procedure for ptychography, using conjugate gradients and including preconditioning strategies, regularization and typical modifications of the statistical noise model. The optimization principle is compared to a difference map reconstruction algorithm. With simulated data important improvements are observed, as measured by a strong increase in the signal-to-noise ratio. Significant gains in resolution and sensitivity are also demonstrated in the ML refinement of a reconstruction from experimental x-ray data. The immediate consequence of our results is the possible reduction of exposure, or dose, by up to an order of magnitude for a reconstruction quality similar to iterative algorithms currently in use.