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In order to solve the problem of weakening the details and the edges of image while denoising in the contourlet domain, this paper presents an adaptive denoising algorithm with detail enhancement and applies it to the denoising procedure of infrared image. On the basis of the assumption that the prior distribution of the original image coefficients and the noise's are both Gaussian in the contourlet...
An adaptive Bayesian estimator for image denoising in shearlet domain is presented, where the normal inverse Gaussian (NIG) distribution is used as the prior model of shearlet coefficients of images. The normal inverse Gaussian distribution can model a wide range of processes, from heavy-tailed to less heavy-tailed processes. Under this prior, a Bayesian shearlet estimator is derived by using the...
In this paper we propose a novel iterative algorithm for wavelet-based image denoising following a Maximum a Posteriori (MAP) approach. The wavelet shrinkage problem is modeled according to the Bayesian paradigm, providing a strong and extremely flexible framework for solving general image denoising problems. To approximate the MAP estimator, we propose GSAShrink, a modified version of a known combinatorial...
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...
The performance of various estimators, such as minimum mean square error (MMSE) is strongly dependent on correctness of the proposed model for original data distribution. Therefore, the selection of a proper model for distribution of wavelet coefficients is important in wavelet based image denoising. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients...
This paper presents image denoising methods performed within wavelet domain scheme by using wavelet packet zerotrees, and at the same time incorporating neighbor and inter-subband dependencies through NeighShrink and BiShrink [1] shrinkage functions, respectively. In particular, we call our proposed method as adaptive wavelet bivariate maximum a posteriori estimator (MAP) with NeighShrink threshold...
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,...
This paper presents a new video denoising algorithm based on the modeling of wavelet coefficients in each subband with a bivariate Cauchy probability density function (pdf). This bivariate pdf takes into account the statistical dependency of wavelet coefficients in adjacent scales. Within this framework, we describe a novel method for video denoising based on designing a maximum a posteriori (MAP)...
The performance of estimators, such as maximum a posteriori (MAP), is strongly dependent on the accuracy of the employed distribution for the noise-free data and the accuracy of the involving parameters. In this paper, we select a proper model for the distribution of wavelet coefficients and present a new image denoising algorithm. We model the wavelet coefficients in each subband with a mixture of...
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