Image denoising using a scale mixture of Gaussians in the wavelet domain

Image denoising using a scale mixture of Gaussians in the wavelet domain (2003)

by Javier Portilla , Vasily Strela , Martin J. Wainwright , Eero P. Simoncelli

Venue:	IEEE Trans Image Processing
Citations:	239 - 16 self

Datum	Value	Source
TITLE	Image denoising using a scale mixture of Gaussians in the wavelet domain	INFERENCE
AUTHOR NAME	Javier Portilla	SVM HeaderParse 0.2
AUTHOR NAME	Vasily Strela	SVM HeaderParse 0.2
AUTHOR NAME	Martin J. Wainwright	SVM HeaderParse 0.2
AUTHOR NAME	Eero P. Simoncelli	SVM HeaderParse 0.2
ABSTRACT	Abstract—We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error.	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
VENUE	IEEE Trans Image Processing	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
CITATIONS	58 found	ParsCit 1.0