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The SONAR images are perturbed by a multiplicative noise calIed 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 waveiets 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. A blind specklesuppression method tbat performs a nonlinear operation on the data, based on B new bishrink filter variant is obtained. Finally, some simulation examples prove the performances of the proposed denoising method. These performances are compared with the results obtained applying state-of-the-art speckle reduction techniques.

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The performance of image denoising algorithms using The multi-resolution analysis performed by the wavelet the Double Tree Complex Wavelet Transform, DT CWT, followed transform, (WT) has been shown to be a powerful tool to by a local adaptive bishrink filter can be improved by reducing achieve good denoising. In the wavelet domain, the noise is the sensitivity of that filter with the local marginal variance of the uniformly spread throughout the coefficients, while most of the wavelet coefficients. In this paper is proposed a solution for the . . . . . t sensitivity reduction based on enhanced diversity. First the imageiinforation wisconcentrtedeinateofew lagest one advantages and disadvantages of a state-of-the-art denoising (sparsity of the wavelet representation), [4-7]. The solution, based on the association DT CWTbishrink filter are corresponding denoising methods have three steps, [1]: 1) The highlighted. Second a blind noise-suppression method correcting computation of the forward WT, 2) the filtering of the wavelet the disadvantages of the bishrink filter, performing a non-linear coefficients, 3) the computation of the inverse wavelet operation on the data is obtained. Finally, some simulation transform of the result obtained, (IWT). Numerous WTs can be examples prove the performances of the proposed denoising used to operate these treatments. The first one was the Discrete method. Wavelet Transform, DWT, [1]. It has three main disadvantages, [8]: lack of shift invariance, lack of symmetry