Journal ArticleDOI
Multivariate statistical modeling for image denoising using wavelet transforms
Dongwook Cho,Tien D. Bui +1 more
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TLDR
The general estimation rule in the wavelet domain is derived to obtain the denoised coefficients from the noisy image based on the multivariate statistical theory and a parametric multivariate generalized Gaussian distribution model is defined which closely fits the sample distribution.Abstract:
Recently a variety of efficient image denoising methods using wavelet transforms have been proposed by many researchers. In this paper, we derive the general estimation rule in the wavelet domain to obtain the denoised coefficients from the noisy image based on the multivariate statistical theory. The multivariate distributions of the original clean image can be estimated empirically from a sample image set. We define a parametric multivariate generalized Gaussian distribution (MGGD) model which closely fits the sample distribution. Multivariate model makes it possible to exploit the dependency between the estimated wavelet coefficients and their neighbours or other coefficients in different subbands. Also it can be shown that some of the existing methods based on statistical modeling are subsets of our multivariate approach. Our method could achieve high quality image denoising. Among the existing image denoising methods using the same type of wavelet (Daubechies 8) filter, our results produce the highest peak signal-to-noise ratio (PSNR).read more
Citations
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Journal ArticleDOI
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
TL;DR: A novel approach to image filtering based on the shape-adaptive discrete cosine transform is presented, in particular, image denoising and image deblocking and deringing from block-DCT compression and a special structural constraint in luminance-chrominance space is proposed to enable an accurate filtering of color images.
Journal ArticleDOI
Image denoising in the wavelet domain using a new adaptive thresholding function
TL;DR: The simulation results show that the proposed thresholding function has superior features compared to conventional methods when used with the proposed adaptive learning types, which makes it an efficient method in image denoising applications.
Journal ArticleDOI
Parameter Estimation For Multivariate Generalized Gaussian Distributions
TL;DR: It is proved that the maximum likelihood estimator (MLE) of the scatter matrix exists and is unique up to a scalar factor, for a given shape parameter β ∈ (0,1).
Journal ArticleDOI
Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function
TL;DR: Experimental results show that the proposed algorithm, called OLI-Shrink, yields better peak signal noise ratio and superior visual image quality—measured by universal image quality index—compared to standard denoising methods, especially in the presence of high noise intensity.
Journal ArticleDOI
Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms
TL;DR: In the framework of texture image retrieval, a new family of stochastic multivariate modeling is proposed based on Gaussian Copula and wavelet decompositions that takes advantage of the copula paradigm to separate dependence structure from marginal behavior.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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De-noising by soft-thresholding
TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
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Adapting to Unknown Smoothness via Wavelet Shrinkage
TL;DR: In this article, the authors proposed a smoothness adaptive thresholding procedure, called SureShrink, which is adaptive to the Stein unbiased estimate of risk (sure) for threshold estimates and is near minimax simultaneously over a whole interval of the Besov scale; the size of this interval depends on the choice of mother wavelet.