SAR despeckling using a modified wavelet-domain statistic model
Xin Zhao, Zengliang Li, Qiuze Yu, Yufan Wang · 2011
This paper proposes a new method for SAR (Synthetic Aperture Radar) image despeckling based on statistical model of wavelet coefficients combined with modification to them according to maximum-modulus criterion. In the method, wavelet coefficients of logarithmic image are firstly modeled as mixture density of two Gaussian (MG) distributions with zero mean. Secondly, in order to incorporate the spatial dependencies into the despeckling procedure, Hidden Markov Tree model (HMT) is explored and Expectation Maximization (EM) algorithm is adopted to estimate model parameters. Bayes Minimum mean square error (Bayes MMSE) method is used to estimate the wavelet coefficients free of noise. The wavelet coefficients are updated according to a criterion whether the coefficient is a significant one or not. 2D inverse DWT and exponential transform are performed on the updated coefficients to get denoised SAR image. Experimental Results using real SAR images demonstrate that the method can not only reduce the speckle but also preserve edges and radiometric scatter points.