Superresolution and convergence properties of the expectation-maximization algorithm for maximum-likelihood deconvolution of incoherent images
Superresolution and convergence properties of the expectation-maximization algorithm for maximum-likelihood deconvolution of incoherent images
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DOI：
10.1364/josaa.15.002609
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发表时间：
1998-10-01
影响因子：
1.9
通讯作者：
Conchello, JA
中科院分区：
文献类型：
--
作者：
Conchello, JA
使用非共焦显微镜的计算光学切片显微镜从根本上是有限的，因为光学传递函数，即点扩展函数的傅里叶变换，在空间频率域的圆锥区域上恰好是零。由于这一光学信息的缺失，图像可能是人为的。为了克服这一限制，在带外推意义上的超分辨是必要的。给出了最大似然图像估计的期望最大化算法的频域分析，说明了该算法是如何实现这种频带外推的。该分析给出了恢复图像的理论绝对带宽；然而，在许多情况下，该绝对值可能并不现实。然后，在假设高斯点扩散函数和样本函数的情况下，给出了算法的第二个分析，展示了该算法更真实的行为，并证明了它的一些性质。给出了该算法超分辨能力的实验结果。(C)1998年美国光学学会。
Computational optical-sectioning microscopy with a nonconfocal microscope is fundamentally limited because the optical transfer function, the Fourier transform of the point-spread function, is exactly zero over a conic region of the spatial-frequency domain. Because of this missing cone of optical information, images are potentially artifactual. To overcome this limitation, superresolution, in the sense of band extrapolation, is necessary. I present a frequency-domain analysis of the expectation-maximization algorithm for maximum-likelihood image estimation that shows how the algorithm achieves this band extrapolation. This analysis gives the theoretical absolute bandwidth of the restored image; however, this absolute value may not be realistic in many cases. Then a second analysis is presented that assumes a Gaussian point-spread function and a specimen function and shows more realistic behavior of the algorithm and demonstrates some of its properties. Experimental results on the superresolving capability of the algorithm are also presented. (C) 1998 Optical Society of America.