Fast maximum-likelihood image-restoration algorithms for three-dimensional fluorescence microscopy
Fast maximum-likelihood image-restoration algorithms for three-dimensional fluorescence microscopy
复制标题
DOI：
10.1364/josaa.18.001062
复制
发表时间：
2001-05-01
影响因子：
1.9
通讯作者：
Conchello, JA
中科院分区：
文献类型：
--
作者：
Markham, J;Conchello, JA
我们已经评估了三个约束，迭代恢复算法，找到一个快速，可靠的算法最大似然估计的荧光显微图像。两种算法使用高斯近似泊松统计，与方差计算假设泊松噪声的图像。第三种方法是使用Csiszar的信息发散。二、发散!差异测度每种方法都包括一个非负约束和一个正则化惩罚项;优化是用共轭梯度法进行的。模拟以及生物图像的方法的性能进行了分析，并与期望最大化最大似然（EM-ML）算法得到的结果进行了比较。基于I-divergence的算法收敛速度最快，并且产生的图像与EM-ML恢复的图像相似，这是通过几个指标来衡量的。对于无噪声模拟样本，EM-X; IL方法达到给定对数似然值所需的迭代次数约为基于I发散度的方法达到相同值所需次数的平方。(C)2001年美国光学学会。
We have evaluated three constrained, iterative restoration algorithms to find a fast, reliable algorithm for maximum-likelihood estimation of fluorescence microscopic images. Two algorithms used a Gaussian approximation to Poisson statistics, with variances computed assuming Poisson noise far the images. The third method used Csiszar's information-divergence. II-divergence! discrepancy measure. Each method included a nonnegativity constraint and a penalty term for regularization; optimization was performed with a conjugate gradient method. Performance of the methods was analyzed with simulated as well as biological images and the results compared with those obtained with the expectation-maximization-maximum-likelihood (EM-ML) algorithm. The I-divergence-based algorithm converged fastest and produced images similar to those restored by EM-ML as measured by several metrics. For a noiseless simulated specimen, the number of iterations required for the EM-X;IL method to reach a given log-likelihood value was approximately the square of the number required for the I-divergence-based method to reach the same value. (C) 2001 Optical Society of America.