NDLI: SAR denoising using wavelet-domain statistic model and maximum-modulus criterion

Content Provider	IEEE Xplore Digital Library
Author	Yu, Qiuze Lv, Wentao Yu, Wenxian Peilin Liu
Copyright Year	2011
Description	Author affiliation: School of electronics, information and electric engineering, Shanghai Jiao Tong University, China (Yu, Qiuze; Lv, Wentao; Yu, Wenxian; Peilin Liu)
Abstract	This paper presents a new method for SAR (Synthetic Aperture Radar) image denoising based on statistical model of wavelet coefficients and their modification by maximum-modulus criterion. First, wavelet coefficients of logarithmic image are modeled as Mixture density of two Gaussian (MG) distributions with zero mean. Second, 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 recalculated according to a criterion whether they are significant ones or not. 2D inverse Discrete Wavelet Transform (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 not only reduces the speckle but also preserve edges and radiometric scatter points.
Starting Page	3696
Ending Page	3699
File Size	775592
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6050027
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Wavelet coefficients Noise reduction Speckle Noise Adaptation models maximum-modulus criterion denoising wavelet domain statistic model
Content Type	Text
Resource Type	Article

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