Publication

SURE-LET multichannel image denoising: Undecimated wavelet thresholding

Thierry Blu, Florian Luisier
2008
Conference Papers

Résumé

We propose an extension of the recently devised SURE-LET grayscale denoising approach for multichannel images. Assuming additive Gaussian white noise, the unknown linear parameters of a transform-domain pointwise multichannel thresholding are globally optimized by minimizing Stein's unbiased MSE estimate (SURE) in the image-domain.

Source officielle

https://infoscience.epfl.ch/entities/publication/bb2b312f-ad6e-4a0a-b6c5-69741840d471

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Thierry Blu, Florian Luisier
2008
Conference Papers

Résumé

Source officielle

https://infoscience.epfl.ch/entities/publication/bb2b312f-ad6e-4a0a-b6c5-69741840d471

À propos de ce résultat

Concepts associés (25)

Modèle linéaire généralisé

En statistiques, le modèle linéaire généralisé (MLG) souvent connu sous les initiales anglaises GLM est une généralisation souple de la régression linéaire. Le GLM généralise la régression linéaire en permettant au modèle linéaire d'être relié à la variable réponse via une fonction lien et en autorisant l'amplitude de la variance de chaque mesure d'être une fonction de sa valeur prévue, en fonction de la loi choisie.

Global optimization

Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. It is usually described as a minimization problem because the maximization of the real-valued function is equivalent to the minimization of the function . Given a possibly nonlinear and non-convex continuous function with the global minima and the set of all global minimizers in , the standard minimization problem can be given as that is, finding and a global minimizer in ; where is a (not necessarily convex) compact set defined by inequalities .

Linear least squares

Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods. The three main linear least squares formulations are: Ordinary least squares (OLS) is the most common estimator.

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In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

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