Speckle reduction using module-maximum-based modification in wavelet domain
Shichun Peng, Jian Liu, Guoping Yan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Speckle noise in synthetic aperture radar (SAR) images is characterized as multiplicative random noise. To address SAR image speckle denoising, this paper proposes a new method which is based on the combination of statistical model of wavelet coefficients and modification to the coefficients according to module-maximum-based (significant coefficient) rule. In our method, wavelet coefficients of image are firstly modeled as mixture density of two Gaussian (MG) distributions with zero mean. In order to incorporate the spatial dependencies into the denoising procedure, hidden markov tree (HMT) model is explored and expectation maximization (EM) algorithm is proposed 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 rule whether the coefficient is a significant one or not. 2D inverse DWT is performed on the updated coefficients to get denoised SAR image. Experimental Results using real SAR image demonstrate that the method can not only reduce the speckle but also preserve edges and radiometric scatter points. Equivalent Number of Look Enl shows that the proposed method yields very satisfactory results compared with other methods.