Statistical estimation techniques for the wavelet-based image denoising use suitable probability density functions (PDFs) as prior functions for the image coefficients. Due to the intrascale dependency of the local neighboring image wavelet coefficients, the prior functions are assumed to be stationary. In this paper, it is shown that the stationary Gram-Charlier (GC) PDF models the image coefficients better than the traditional ones, such as the stationary Gaussian and stationary generalized Gaussian PDFs. A Bayesian wavelet-based maximum a posteriori estimator is then developed by using the proposed GC prior function. Experimental results on standard images show that the proposed estimator provides a denoising performance, which is better than that of several existing denoising methods in terms of signal-to-noise ratio and visual quality.