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The distortion of images by additive white Gaussian noise (AWGN) is common during its acquisition, processing, compression, storage, transmission, and reproduction. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. Indeed, one of the cruxes of the Bayesian image denoising algorithms is to estimate the local variance of the image. Here, we employ...
In this paper, we present a novel speckle removal algorithm within the framework of Bayesian estimation and wavelet analysis. The proposed method to apply a logarithmic transformation to convert speckel, multiplicative, noise model into an additive noise model. The subband decomposition of logarithmically transformed image are the best described by a family of heavy-tailed densities such as Two-Sided...
The dual-tree complex wavelet transform has been proposed as a novel analysis tool featuring near shift-invariance and improved directional selectivity compared to the standard wavelet transform. Within this framework, we describe a novel technique for removing AWGN, additive white Gaussian noise, from digital image. In this paper, we design multivariate maximum a posterior (MAP) estimator, which...
This paper is concerned with wavelet-based image denoising using Bayesian technique. In conventional denoising process, The parameters of probability density function (PDF) are usually calculated from the first few moments, mean and variance. In this work, a new image denoising algorithm based on Pearson Type IV random vectors is proposed. Pearson Type IV is used because it allows higher-order moments...
In this work, we present new Bayesian estimator for spherically-contoured Two-Sided Gamma random vectors in additive white Gaussian noise (AWGN). This PDF is used in view of the fact that it is more peaked and the tails are heavier to be incorporated in the probabilistic modeling of the wavelet coefficients. One of the cruxes of the Bayesian image denoising methods is to estimate statistical parameters...
This paper concerns wavelet-based image denoising using Bayesian technique. The conventional probability density function (PDF) used in the denoising process usually has parameters that are calculated from the first few moments only (for example, mean and variance). In this work, a new image denoising algorithm based on bivariate Pearson Type VII distribution is presented. This PDF is used in view...
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