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Wavelet Domain LMMSE-Like Denoising Algorithm Based on GGD ML Estimation |
Li Jun-xia; Shui Peng-lang |
National Lab. of Radar Signal Processing, Xidian University, Xi’an 710071, China |
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Abstract Based on the assumption that wavelet coefficients obey Generalized Gaussian Distribution (GGD), this paper adopts Maximum Likelihood (ML) principle to estimate wavelet coefficients variance of common images in sub-bands. The proposed estimator is product of a sub-band adjustable factor and a power mean factor. Compared to the recently proposed SI-AdaptShr, LAWMAP and other wavelet-based methods, better de-noising results may be obtained for the proposed method. Furthermore, a simplified algorithm is also formed to de-speckle SAR images. It is shown that the new method may remarkably reduce the calculation amount and helpful for the post-processing of large scale SAR images.
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Received: 21 April 2006
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