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JEI
2008
100views more  JEI 2008»
14 years 11 months ago
Context adaptive image denoising through modeling of curvelet domain statistics
We perform a statistical analysis of curvelet coefficients, distinguishing between two classes of coefficients: those that contain a significant noise-free component, which we call...
Linda Tessens, Aleksandra Pizurica, Alin Alecu, Ad...
TIP
2008
163views more  TIP 2008»
14 years 11 months ago
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures
We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as ...
David K. Hammond, Eero P. Simoncelli
IJCV
2010
206views more  IJCV 2010»
14 years 10 months ago
From Local Kernel to Nonlocal Multiple-Model Image Denoising
Abstract We review the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-d...
Vladimir Katkovnik, Alessandro Foi, Karen Egiazari...
CORR
2011
Springer
282views Education» more  CORR 2011»
14 years 6 months ago
Fast Linearized Bregman Iteration for Compressive Sensing and Sparse Denoising
We propose and analyze an extremely fast, efficient and simple method for solving the problem: min{ u 1 :Au=f,u∈Rn }. This method was first described in [1], with more details i...
Stanley Osher, Yu Mao, Bin Dong, Wotao Yin
ICIP
2001
IEEE
16 years 1 months ago
The curvelet transform for image denoising
We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform [3] and the curvelet transform [7, 6]. Our implementations offer...
Emmanuel J. Candès