Maximum-Entropy Expectation-Maximization Algorithm for Image Processing and Sensor Networks

摘要

In this paper, we propose a maximum-entropy expectation-maximization algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed in order to ensure smoothness of the estimated density function. The exact derivation of the maximum-entropy expectation-maximization algorithm requires determination of the covariance matrix combined with the maximum entropy likelihood function, which is difficult to solve directly. We therefore introduce a new lower-bound for the EM algorithm derived by using the Cauchy-Schwartz inequality to obtain a suboptimal solution. We use the proposed algorithm for function interpolation and image segmentation. We propose the use of the EM algorithm for image recovery from randomly sampled data and signal reconstruction from randomly scattered sensors. We further propose to use our approach to maximum-entropy expectation-maximization (MEEM) in all of these applications. Computer simulation experiments are used to demonstrate the performance of our algorithm in comparison to existing methods.