Title :
Despeckling SAR Images Using Bayes-Shrink in Bandelet Domain
Author :
Xu, Yanan ; Gao, Qingwei ; Lu, Yixiang ; Zhong, Weinian
Author_Institution :
Key Lab. of Intell. Comput. & Signal Process., Anhui Univ., Hefei, China
Abstract :
Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of scattering phenomena. This paper presents a despeckling method for SAR images based on adaptive bandelets thresholding. This threshold is derived in a Bayesian framework. The proposed threshold is simple and closed form, and it is applied to adaptive bandelets coefficients to achieve more satisfying results. The performances of adaptive bandelets Bayes-shrink soft-thresholding and wavelet thresholding for despeckling SAR images are compared through an experiment. Experiment results clearly demonstrated the capability of the proposed scheme in SAR image speckle reduction especially for SAR images possessing detailed textures.
Keywords :
electromagnetic wave scattering; image denoising; image segmentation; image texture; radar imaging; synthetic aperture radar; wavelet transforms; Bayes-shrink soft-thresholding; Bayesian framework; SAR image despeckling method; adaptive bandelet thresholding; image denoising; image texture; scattering phenomena; synthetic aperture radar; wavelet thresholding; Discrete wavelet transforms; Educational technology; Geometry; Laboratories; Noise reduction; Radar scattering; Radar signal processing; Speckle; Synthetic aperture radar; Wavelet domain;
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
DOI :
10.1109/CISP.2009.5301474