Bivariate shrinkage with local variance estimation

Sendur, L.; Selesnick, Ivan

doi:10.1109/lsp.2002.806054

Cited by 513 publications

(307 citation statements)

References 13 publications

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“…The second experiment is to compare the proposed approach with other seven denoising methods [12,7,3,6,8,13,9], which are implemented using Method Barbara W indow Lighthouse σ n = 10 σ n = 20 σ n = 10 σ n = 20 σ n = 10 σ n = 20 Ref. [12] 30 [12,7,3,6,8,13,9], respectively; and (j) proposed method that outperforms the above seven methods.…”

Section: Resultsmentioning

confidence: 99%

“…[12] 30 [12,7,3,6,8,13,9], respectively; and (j) proposed method that outperforms the above seven methods.…”

Section: Resultsmentioning

confidence: 99%

“…Firstly, since the noise is assumed to be additive white Gaussian noise with a zero mean and a standard deviation σ n , the term p(y i |s i ) is formulated as [7,6,8,9] …”

Section: Proposed Image Denoising Approachmentioning

confidence: 99%

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A wavelet-domain non-parametric statistical approach for image denoising

Tian

Chen

2010

IEICE Electron. Express

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Abstract:The challenge of conventional parametric model-based wavelet image denoising approaches is that the efficiency of these methods greatly depends on the accuracy of the prior distribution used for modeling the wavelet coefficients. To tackle the above challenge, a non-parametric statistical model is proposed in this paper to formulate the marginal distribution of wavelet coefficients. The proposed non-parametric model differs from conventional parametric models in that the proposed model is automatically adapted to the observed image data, rather than imposing an assumption about the distribution of the data. Furthermore, the proposed non-parametric model is incorporated into a Bayesian inference framework to derive a maximum a posterior (MAP) estimation-based image denoising approach. Experiments are conducted to not only demonstrate that the proposed non-parametric statistical model is more suitable than conventional models to formulate the marginal distribution of wavelet coefficients, but also show that the proposed image denoising approach outperforms the conventional approaches.

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Section: Resultsmentioning

confidence: 99%

“…[12] 30 [12,7,3,6,8,13,9], respectively; and (j) proposed method that outperforms the above seven methods.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A wavelet-domain non-parametric statistical approach for image denoising

Tian

Chen

2010

IEICE Electron. Express

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“…This process continues until the required level is reached. Some more methods have been proposed based on statistical modeling of wavelet coefficients [15,16]. By extending the idea of Cai and Silverman for 2D image case, Chen et al [17] proposed new method namely NeighShrink which thresholds the wavelet coefficients according to the sum of the squares of all the wavelet coefficients within a neighborhood window.…”

Section: ˩J ŵ ˩˦ éé ˮ˨Jmentioning

confidence: 99%

Performance Analysis of InterpolatedShrink method in Image De-Noising

Bhat¹,

Jagadale²

2011

IJCA

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The de-noising of an image corrupted by Gaussian noise is a classical problem in signal or image processing. An image is often corrupted by noise during its acquisition and transmission. Image de-noising is used to reduce the noise while retaining the important features in the image. Always there exists a tradeoff between the removed noise and the blurring in the image. The use of wavelet transform for signal de-noising has emerged as an important technique during the last decade. The wavelet transform is preferred over conventional Fast Fourier Transform(FFT) based image denoising technique ,because of its capability to give detailed spatial-frequency information. In this paper, we tried to analyze the performance of InterpolatedShrink method in image de-noising using various wavelet family, such as Haar,Doubechies,Symlet and Coiflets, for Gaussian noise. General TermsComputer Science-Image Processing.

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“…The results for this technique are reported for the undecimated wavelet transform, with Symlet 8, as in [9]. The BiShrink method of [16] estimates the noise-free wavelet coefficients based on a bivariate statistical model for wavelet coefficients and their parent coefficients, in the DT-CWT domain. The GSM-BLS filter [17], already explained before, uses Full Steerable Pyramids (FSP), with 8 orientations and a 3 × 3 local window, without inclusion of a parent coefficient in the local neighbourhood.…”

Section: B Using Data-driven Bases Of Principal Componentsmentioning

confidence: 99%

Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

Goossens

Pižurica

Philips

2009

IEEE Trans. on Image Process.

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Abstract-We propose a new statistical model for image restoration in which neighbourhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighbourhood is obtained, thereby modeling the strongest correlations in that neighbourhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.

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Bivariate shrinkage with local variance estimation

Cited by 513 publications

References 13 publications

A wavelet-domain non-parametric statistical approach for image denoising

A wavelet-domain non-parametric statistical approach for image denoising

Performance Analysis of InterpolatedShrink method in Image De-Noising

Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures

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