We propose a contextual hidden Markov tree (CHMT) model by adding intrascale dependences in the hidden Markov tree (HMT) model to capture more wavelet clustering property and apply the model for SAR image despeckling. Instead of directly adding the transition probabilities between two adjacent hidden states in the HMT model, we add transition probabilities between hidden states of a wavelet coefficient and several hidden states of the virtual coefficients that are duplicated from the adjacent coefficients of the considered coefficient, such that the merit of the HMT model is kept, and the persistent and clustering properties of wavelet coefficients are completed described in the model. In experiments, the proposed CHMT model produced better results than the HMT model produced for image despeckling. Furthermore, with the same results, the CHMT model needs fewer iterations than the HMT model needs.
|Number of pages||3|
|State||Published - 2003|
|Event||2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France|
Duration: 21 Jul 2003 → 25 Jul 2003
|Conference||2003 IGARSS: Learning From Earth's Shapes and Colours|
|Period||21/07/03 → 25/07/03|