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SAR Images Despeckling Based on Model Analysis in Wavelet Domains

Author: WangXia
Tutor: WuYan
School: Xi'an University of Electronic Science and Technology
Course: Circuits and Systems
Keywords: Wavelet Transform Minimum mean square error (MMSE) Bayesian estimation Hidden Markov Chain (HMC) Hidden Markov tree (HMT)
CLC: TN957.52
Type: Master's thesis
Year: 2006
Downloads: 22
Quote: 0
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Abstract


Inhibition of SAR image speckle technology has been an important issue in the SAR imaging processing and image analysis . Based on the model in the wavelet domain , two kinds the SAR Image Despeckling algorithm , simulation results show that the two algorithms have achieved the desired effect . Based the smooth wavelet transform the Bayesian fuzzy atrophy SAR image speckle suppression algorithm . Derived signal based on Bayesian estimation minimum mean square error (MMSE) fuzzy shrinking factor analysis of the statistical model of the SAR images in the a smooth wavelet transform domain . Whereby adjacent scale wavelet coefficients of correlation , using the sub-regional atrophy fuzzy thinking , well no spots real signal wavelet coefficients estimated . SAR image despeckling algorithm of wavelet domain hidden Markov mixture model . The model takes into account the wavelet coefficients and clustering , hidden Markov tree (HMT) and hidden Markov chain (HMC) , respectively portray . And half tree model improves the HMT algorithm running speed , HMC can more accurately estimate the initial value of the model root . The algorithm fusion the Bayesian MMSE noise suppression technology , no spots real signal wavelet coefficients estimated .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radar > Radar equipment,radar > Radar receiving equipment > Data,image processing and admission
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