A novel method for SAR image denoising based on HMT in complex wavelet pocket transform domain

A novel SAR image denoising scheme based on hidden Markov tree (HMT) in the quad-tree complex wavelet packet transform (QCWPT) domain was presented to achieve the tradeoff between details retainment and noise removal. A neighborhood coefficient differential window was used to compute intra-scale correlations of complex wavelet coefficients in high frequency detail subimage,and intra-scale correlational state was identified according to the smallest error rate Bayesian decision-making rules. A HMT was fitted to describe the correlations between the complex wavelet coefficients across decomposition scales and mark inter-scale correlational state. The product results of corresponding positional intra-scale and inter-scale correlational state were looked as a new hidden state transition probability. A set of iterative equations was developed using the expectation-maximization(EM) algorithm to estimate the model parameters and produce denoising images. Experimental results show that the proposed denoising algorithm is superior to the traditional filtering methods and possible to achieve an excellent balance between suppress speckle noise effectively and preserve as many image details and edges as possibly.