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Wavelet domain hidden Markov models (WHMMs) provide a powerful approach for image modeling and processing because of the clustering and persistence properties of wavelet coefficients. However, the shift-variance of real wavelet transforms degrades the accuracy of the WHMMs. To overcome this problem, we propose a hidden Markov model based on the dual-tree complex wavelet transform that is approximately shift-invariant. Context information is used in this model to indicate the local correlation among wavelet coefficients. According to different visual attributes, several contexts based on frequency, orientation and scale are applied to capture both intrascale and interscale dependencies. The parameters of this model are estimated by an EM algorithm. Applications to image denoising are presented. The denoising performance is among the best state-of-the-art techniques, and outperforms models, which are based on real discrete wavelet transforms (DWTs).