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In wavelet-based Bayesian denoising, the performance of several methods strongly depends on the correctness of the distribution that is used to describe the data. Therefore, the selection of a proper model for distribution is thus an important issue in the denoising process. This paper presents a new image denoising algorithm based on bivariate Pearson type VII distribution with approximated MAP estimation...
Addressing SAR image speckle denoising, this dissertation proposes a new method based on bivariate shrinkage function combined with enhancement of wavelet significant coefficients, which allows us to consider the dependencies between coefficients. In our paper we make the speckle noise model suit the bivariate shrinkage function, and the joint probability density functions (PDF) and noise PDF could...
This paper presents image-denoising methods performed within wavelet domain scheme by incorporating neighboring coefficients, namely NeighShrink (G.Y. Chen et al., 2004), and at the same time, denoising the image with bivariate shrinkage function. The idea of bivariate shrinkage function (BiShrink (L. Sendur and I.W. Selesnick, 2002)) is to model the signal based on MAP estimation approach. In fact,...
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