Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation
Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation
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DOI：
10.1109/tip.2008.921996
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发表时间：
2008-06
影响因子：
10.6
通讯作者：
Hunsop Hong;D. Schonfeld
中科院分区：
文献类型：
--
作者：
Hunsop Hong;D. Schonfeld
在本文中，我们提出了一个最大熵期望最大化（MEEM）算法。我们使用该算法的密度估计。最大熵约束的平滑估计的密度函数施加。MEEM算法的推导需要在最大熵似然函数的框架中确定协方差矩阵，这很难解析地解决。因此，我们通过优化最大熵似然函数的下界来推导MEEM算法。我们注意到，经典的期望最大化（EM）算法已被采用以前的2-D密度估计。我们建议扩展使用的经典EM算法的图像恢复随机采样数据和传感器场估计随机分散的传感器网络。我们进一步建议使用我们的方法在密度估计，图像恢复和传感器场估计。计算机仿真实验证明了所提出的MEEM算法与现有方法相比具有上级性能。
In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods.