Due to the diversity of sample space, it is difficult to calculate the model of the potential function, and therefore difficult to obtain the parameter estimation of Markov random field model. To solve the problem, a parameter estimation algorithm for Markov random field is presented based on Maclaurin series. This algorithm employs a second-order expansion of Maclaurin series to derive the approximate expressions of the potential function and the likelihood function.The system of nonlinear equations corresponding to the Maximum Likelihood Estimation (MLE) is derived, which is calculated by Newton iteration method, and its solution is the MLE for Markov random field. An updated approach is presented and used to calculate the optimal observed value of the degradation model for noisy images based on Gibbs sampling for accelerating the simulation annealing. Tests have verified the effectiveness of the algorithm from three aspects of visual effect, peak signal to noise ratio, and iteration times.