Title :
SAR Images Despeckling via Bayesian Shrinkage Based on Nonsubsampled Contourlet Transform
Author :
Zhang De-xiang ; Gao Qing-wei ; Wu Xiao-pei
Author_Institution :
Key Lab. of Intell. Comput. & Signal Process. of Minist. of Educ., Anhui Univ., Hefei, China
Abstract :
We propose a novel and efficient SAR image despeckling via Bayesian shrinkage based on nonsubsampled contourlet transform, which has been recently introduced. Despeckling by means of contourlet transform introduce many visual artifacts due to the Gibbs-like phenomena. Nonsubsampled contour let transform is a flexible multiscale, multidirection and shift-invariant image decomposition that can be efficiently implemented via transform. A Bayesian estimator is applied to the decomposed contour let coefficients of the logarithmically transformed image to estimate the best value for the noise-free signal. Experimental results show that compared with conventional wavelet despeckling algorithm, the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserves image details, and the significant information of original image like textures and contour details is well maintained.
Keywords :
Bayes methods; estimation theory; radar imaging; speckle; synthetic aperture radar; transforms; Bayesian estimator; Bayesian shrinkage; Gibbs-like phenomena; SAR images despeckling; nonsubsampled contourlet transform; shift invariant image decomposition; Bayesian methods; Filter bank; Noise; Noise reduction; Speckle; Wavelet transforms; Bayesian shrinkage; SAR image; despeckling; nonsubsampled contourlet transform;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
DOI :
10.1109/ICCIS.2010.219