Expectation maximization (EM)algorithm for parameter estimation of image statistical model is one of the striking research fields in recent decades.Based on the analysis of the EM algorithm

combining the current application research in parameter estimation of image statistical model

analysis and comparison are conducted in terms of the three improvement schemes of standard EM algorithm.In this paper

integrating image restoration

segmentation

object tracking and the fusion of other evolution optimization algorithms

through three aspects

such as the selection of missing data sets

the statistical model establishments of missing and incomplete data sets

and parameter estimation of image statistical models

as well as the advantages and disadvantages of the corresponding EM algorithm are exponded.The structure and complexity of EM algorithm

so far as to success or failure

are directly determined by the selection of missing data and the expression form of incomplete data.In the end

challenges and possible trends are discussed

and extensive applications of EM algorithm to parameter estimation of statistical model with missing data are pointed out.