Speckle reduction algorithm for synthetic aperture radar images based on Bayesian maximum a posteriori estimation in wavelet domain

The inherent speckle noise in synthetic aperture radar (SAR) images severely degrades the image interpretation and affects the follow-up image-processing tasks. Thus, speckle suppression is a critical step in SAR image preprocessing. We propose a novel locally adaptive speckle reduction algorithm based on Bayesian maximum a posteriori (MAP) estimation in wavelet domain. First, the presented method performs logarithmical transform to original speckled SAR image and an undecimated wavelet transform (UWT). The Rayleigh distribution is used to model the statistics of the speckle wavelet coefficients, and the Laplacian distribution models the statistics of wavelet coefficients due to signal. A Bayesian estimator with a closed-form solution is derived from MAP criterion estimation, and the resulting formula is proved to be equivalent to soft thresholding in nature, which makes our algorithm very simple. Furthermore, the parameters of the Laplacian model are estimated from the coefficients in a neighboring window, thus making the presented method spatially adaptive in the wavelet domain. Theoretical analysis and simulation experiment results show that the proposed method is simple and effective. It significantly improves the visual quality of the SAR images and yields better performance than spatial filterings and traditional wavelet despeckling algorithms. (c) 2008 Society of Photo-Optical Instrumentation Engineers.