Directionlet-based denoising of SAR images using a Cauchy model

Authors:
Qingwei Gao;Yixiang Lu;Dong Sun;Zhan-Li Sun;Dexiang Zhang
Affiliations:
School of Electrical Engineering and Automation, Anhui University, China;School of Electrical Engineering and Automation, Anhui University, China;School of Electrical Engineering and Automation, Anhui University, China;School of Electrical Engineering and Automation, Anhui University, China;School of Electrical Engineering and Automation, Anhui University, China
Venue:
Signal Processing
Year:
2013

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Abstract

A new denoising algorithm based on directionlet transform using a Cauchy probability density function (PDF) is proposed to remove speckle noise. First, an anisotropic directionlet transform is taken on the logarithmically transformed SAR images. The directionlet transform coefficients of reflectance image are modeled as a zero-location Cauchy PDF, while the distribution of speckle noise is modeled as an additive Gaussian distribution with zero-mean. Then a maximum a posteriori (MAP) estimator is designed using the assumed priori models. And a regression-based method is proposed to estimate the parameters from the noisy observations. Finally, the performance of the proposed algorithm is compared with those of existing despeckling methods applied on both synthetic speckled images and actual SAR images. Experimental results show that the proposed scheme efficiently removes speckle noise from SAR images.