The challenge of conventional parametric model-based wavelet image denoising approaches is that the efficiency of these methods greatly depends on the accuracy of the prior distribution used for modeling the wavelet coefficients. To tackle the above challenge, a non-parametric statistical model is proposed in this paper to formulate the marginal distribution of wavelet coefficients. The proposed non-parametric model differs from conventional parametric models in that the proposed model is automatically adapted to the observed image data, rather than imposing an assumption about the distribution of the data. Furthermore, the proposed non-parametric model is incorporated into a Bayesian inference framework to derive a maximum a posterior (MAP) estimation-based image denoising approach. Experiments are conducted to not only demonstrate that the proposed non-parametric statistical model is more suitable than conventional models to formulate the marginal distribution of wavelet coefficients, but also show that the proposed image denoising approach outperforms the conventional approaches.