Contextual Hidden Markov Tree Model for the Dual-Tree Complex Wavelet Transform

Multiresolution models such as the wavelet domain hidden Markov tree (HMT) provide a powerful approach for image modeling and processing because of the persistence properties of wavelet coefficients. In this paper, a new HMT model based on the dual-tree complex wavelet transform is proposed. The model is extended from the contextual hidden Markov tree (CHMT) to the complex wavelet transform, which can mitigate the two problems (shift-variance and lack of the clustering property of wavelet coefficients within a scale) of the conventional wavelet domain HMT model simultaneously. We demonstrate the effectiveness of the model for image denoising. Experiments show that this new model achieves better performance than other related HMT-based image denoising algorithms.