The hidden Markov tree (HMT) model is a novel statistical model for image processing on wavelet domain. The HMT model captures the persistence property of wavelet coefficients, but lacks the clustering property of wavelet coefficients within a scale. We propose the contextual hidden Markov tree (CHMT) model to enhance the clustering property of the HMT model by adding extended coefficients associated with wavelet coefficients. The extended coefficients do not change the wavelet tree structure but enhance the intrascale dependencies of the HMT model. Hence, the training scheme of the HMT model can be modified to estimate the parameters of the CHMT model. In experiments, the proposed CHMT model produces almost better results than the HMT model produces for image denoising. Furthermore, the CHMT model requires fewer iterations of training than the HMT model to achieve the same denoised results.