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Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
The rise of machine learning in image processing has created a gap betwe...
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Relaxing the Gaussian assumption in Shrinkage and SURE in high dimension
Shrinkage estimation is a fundamental tool of modern statistics, pioneer...
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Image Denoising with Kernels based on Natural Image Relations
A successful class of image denoising methods is based on Bayesian appro...
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Denoising and compression in wavelet domain via projection onto approximation coefficients
We describe a new filtering approach in the wavelet domain for image den...
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Blockwise SURE Shrinkage for Non-Local Means
In this letter, we investigate the shrinkage problem for the non-local m...
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Asymmetric prior in wavelet shrinkage
In bayesian wavelet shrinkage, the already proposed priors to wavelet co...
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A modeling and algorithmic framework for (non)social (co)sparse audio restoration
We propose a unified modeling and algorithmic framework for audio restor...
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Image denoising through bivariate shrinkage function in framelet domain
Denoising of coefficients in a sparse domain (e.g. wavelet) has been researched extensively because of its simplicity and effectiveness. Literature mainly has focused on designing the best global threshold. However, this paper proposes a new denoising method using bivariate shrinkage function in framelet domain. In the proposed method, maximum aposteriori probability is used for estimate of the denoised coefficient and non-Gaussian bivariate function is applied to model the statistics of framelet coefficients. For every framelet coefficient, there is a corresponding threshold depending on the local statistics of framelet coefficients. Experimental results show that using bivariate shrinkage function in framelet domain yields significantly superior image quality and higher PSNR than some well-known denoising methods.
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