SAR Image Denoising Based on Lifting Directionlet Domain Gaussian Scale Mixtures Model

Abstract

In this paper,a new speckle suppression method for SAR image is proposed.By combining Directionlet transform with a version of the hidden Markov model—Gaussian scale mixtures(GSM),the marginal distributions of neighbor coefficients in the lifting Directionlet domain are modeled.For removing the speckle noise,the Bayes least square estimation is adopted to evaluate each coefficient.Being regarded as a novel multiscale geometrical analysis tool,Directionlet transform retains the separable filtering,computation simplicity and filter design from the standard two-dimensional wavelet transform,which can capture anisotropic geometrical structures efficiently by multi-direction selection.The introduction of lifting scheme reduces computation amount greatly.Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables:A Gaussian vector and a hidden positive scalar multiplier.Under this model,the marginal of neighbor coefficients are well described and the strong correlation among the amplitudes of neighbor coefficients is also presented adequately.Experiments using plentiful real SAR images indicate that the proposed method outperforms the spatial filters and other methods based on wavelets in terms of speckle reduction as well as image detail preservation.