Adaptive Bayesian-based speckle-reduction in SAR images using complex wavelet transform

Ning Ma; Wei Yan; Peng Zhang

doi:10.1117/12.655033

3 November 2005 Adaptive Bayesian-based speck-reduction in SAR images using complex wavelet transform

Ning Ma, Wei Yan, Peng Zhang

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 604331 (2005) https://doi.org/10.1117/12.655033
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

In this paper, an improved adaptive speckle reduction method is presented based on dual tree complex wavelet transform (CWT). It combines the characteristics of additive noise reduction of soft thresholding with the CWT's directional selectivity, being its main contribution to adapt the effective threshold to preserve the edge detail. A Bayesian estimator is applied to the decomposed data also to estimate the best value for the noise-free complex wavelet coefficients. This estimation is based on alpha-stable and Gaussian distribution hypotheses for complex wavelet coefficients of the signal and noise, respectively. Experimental results show that the denoising performance is among the state-of-the-art techniques based on real discrete wavelet transform (DWT).

Citation Download Citation

Ning Ma, Wei Yan, and Peng Zhang "Adaptive Bayesian-based speck-reduction in SAR images using complex wavelet transform", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604331 (3 November 2005); https://doi.org/10.1117/12.655033

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