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Wavelet domain hidden Markov models (HMMs) have been proposed and applied to image processing, e.g., image denoising. We develop a new HMM, called local contextual HMM (LCHMM), by introducing the Gaussian mixture field where wavelet coefficients are assumed to locally follow the Gaussian mixture distributions determined by their neighborhoods. The LCHMM can exploit both the local statistics and the intrascale dependencies of wavelet coefficients at a low computational complexity. We show that the LCHMM combined with the "cycle-spinning" technique can achieve state-of-the-art image denoising performance.