A Poisson noise image denoising method based on Bayesian probability model was proposed. An image denoising model was constructed based on Bayesian maximum a posteriori probability model and with combination of Poisson probability distribution. Considering that Markov random fields cannot represent complex natural images effectively, a higher-order Markov fields of experts was introduced as a prior regular term of the model to represent the probability distribution of the image. The quadratic penalty function was used to optimize the denoising model and restore clear images. The proposed method was compared with other denoising algorithms; the denoising effect was evaluated objectively by using two evaluation indexes: peak signal-to-noise ratio and structural similarity. The experimental results show that, compared with the traditional denoising methods, the peak signal-to-noise ratio of this method increased by at least 0.18 dB, and the denoising performance is significantly better than that of other methods. Thus, the details of the image can be retained better by using this mothed.