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A new color image denoising method in the contourlet domain is proposed for reducing noise in images corrupted by Gaussian noise. This method takes into account the statistical dependencies among the contourlet coefficients of the RGB color channels. To this end, the multivariate Cauchy distribution is employed to capture these inter-channel dependencies. This model is then exploited in a Bayesian...
Despecking is an essential part of any synthetic aperture radar (SAR) imagery systems. In this work, we propose a new despeckling method for SAR images in the wavelet domain. The performance of a method can be significantly improved by taking into account the statistical dependencies between the wavelet coefficients. It has been shown that the vector-based hidden Markov model (HMM) is capable of capturing...
Speckle noise reduction is a prerequisite task in images captured by ultrasonography systems due to their inherent noisy nature. In this work, we propose a new despeckling method in the contourlet domain using the Cauchy prior. The multiplicative speckle noise is first transferred to an additive one using a logarithmic transform. The logarithmically-transformed contourlet coefficients of the image...
A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. This method takes into account the statistical dependencies among the contourlet coefficients of different scales. In view of this, a non-Gaussian multivariate distribution is proposed to capture the across-scale dependencies of the contourlet coefficients. This model is then exploited...
Speckle reduction has been a prerequisite for many SAR image processing tasks. This work presents a new approach for despeckling of SAR images in the contourlet domain using the alpha-stable distribution. It is shown that the alpha-stable distribution provides a good fit for the contourlet coefficients of an image, since it can capture the large peak and heavy tails of the distribution of the empirical...
Statistical image modeling has attracted great attention in the field of image denoising. In this work, a new image denoising method in the contourlet domain is introduced in which the contourlet coefficients of images are modeled by using the Bessel k-form prior. A noisy image is decomposed into a low frequency approximation sub-image and a series of high frequency detail sub-images at different...
In this paper, we propose an order statistics-based unbiased homomorphic system to reduce multiplicative noise. The design of such a system is based on the probability density function (PDF) of the noise. First, we generalize the order statistics-based nonlinear filter called the sampled function weighted order (SFWO) filter proposed in [1] to reduce additive noise, to the case when the additive noise...
In this paper, a new contourlet-based method for denoising of images corrupted by additive white Gaussian noise is proposed. The alpha-stable distribution is used to model the contourlet coefficients of noise-free images. This model is then exploited to develop a Bayesian minimum mean absolute error estimator. A modified empirical characteristic function-based method is employed for estimating the...
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