Multi-scale MAP denoising of SAR images

The SAR images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. The use of speckle reduction filters is necessary to optimize the images exploitation procedures. This paper presents a new speckle reduction method in the wavelets domain using a novel Bayesian-based algorithm, which tends to reduce the speckle, preserving the structural features (like the discontinuities) and textural information of the scene and a new discrete wavelet transform called Diversity Enhanced Discrete Wavelet Transform, DEDWT. The entire class of diversity improved wavelet transforms is characterized and is proved that the averager optimizes the synthesis step for the minimization of the mean square approximation error. A blind speckle-suppression method that performs a non-linear operation on the data, based on a new bishrink filter variant is obtained. Finally, some simulation examples prove the performance of the proposed denoising method. This performance is compared with the results obtained applying state-of-the-art speckle reduction techniques.