Spatially adaptive radar speckle reduction using wavelet denoising and Markov random fields
Hua Xie, L.E. Pierce, Fawwaz T. Ulaby · 2002
In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-fields-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The EM (expectation-maximization) algorithm is used to estimate hyperparameters which specify the mixture model, and simulated annealing is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of MSE measure. It also achieves better performance than the refined Lee filter in the case of low noise levels.