In this study, we present a wavelet-based method for removing noise from images and a Bayesian shrinkage factor was derived to estimate noise-free wavelet coefficients. This method took into account dependencies between wavelet coefficients. The interscale dependencies were measured from the local singularity and a conditional probability model was proposed. The intrascale dependencies were measured from the spatial clustering properties and a prior probability model was used. Based on these models in a Bayesian framework, each coefficient was modified separately. Experimental results demonstrate this method improves the denoising performance and preserves the details of the image.