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Proceedings Paper

Speckle reduction using module-maximum-based modification in wavelet domain
Author(s): Shichun Peng; Jian Liu; Guoping Yan
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Paper Abstract

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.

Paper Details

Date Published: 4 January 2006
PDF: 6 pages
Proc. SPIE 5985, International Conference on Space Information Technology, 598534 (4 January 2006); doi: 10.1117/12.657932
Show Author Affiliations
Shichun Peng, Huazhong Univ. of Science and Technology (China)
Jian Liu, Huazhong Univ. of Science and Technology (China)
Guoping Yan, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 5985:
International Conference on Space Information Technology
Cheng Wang; Shan Zhong; Xiulin Hu, Editor(s)

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