DocumentCode
2294016
Title
SAR Image Despeckling Using Local Contextual Hidden Markov Model in the Contourlet Domain
Author
Wang, Shuang ; Xu, Xiao ; Hou, Biao ; Jiao, Li Cheng
Author_Institution
Inst. of Intelligent Inf. Process., Xidian Univ., Xi´´an
fYear
2006
fDate
16-19 Oct. 2006
Firstpage
1
Lastpage
4
Abstract
Synthetic aperture radar (SAR) image despeckling is an important problem in the SAR applications. A novel despeckling approach using a local contextual hidden Markov model (LCHMM) in the contourlet domain is presented in this paper. The proposed method can not only use the multiresolution and multidirection characteristics of the contourlet transform, but also exploit the local statistics and capture the intrascale dependencies of the contourlet coefficients by using LCHMM. The experiments in despeckling SAR images show that the proposed method in contrary to other methods can obtain a better trade-off between smoothing the homogeneous areas and keeping the edges and can get better visual effect
Keywords
hidden Markov models; image denoising; image resolution; radar imaging; radar resolution; speckle; synthetic aperture radar; LCHMM; SAR image despeckling; contourlet transform; local contextual hidden Markov model; multiresolution characteristics; synthetic aperture radar; Context modeling; Filter bank; Hidden Markov models; Multiresolution analysis; Radar signal processing; Signal resolution; Smoothing methods; Statistics; Synthetic aperture radar; Wavelet transforms; SAR image despeckling; a local contextual hidden Markov model; contourlet transform;
fLanguage
English
Publisher
ieee
Conference_Titel
Radar, 2006. CIE '06. International Conference on
Conference_Location
Shanghai
Print_ISBN
0-7803-9582-4
Electronic_ISBN
0-7803-9583-2
Type
conf
DOI
10.1109/ICR.2006.343462
Filename
4148463
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