Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-Preserving Image Denoising and Estimation of Discontinuous Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multivariate Statistical Models for Image Denoising in the Wavelet Domain
International Journal of Computer Vision
Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
Automatica (Journal of IFAC)
Wavelet estimation by Bayesian thresholding and model selection
Automatica (Journal of IFAC)
Image denoising in steerable pyramid domain based on a local Laplace prior
Pattern Recognition
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Stochastic super-resolution image reconstruction
Journal of Visual Communication and Image Representation
Image Denoising with Kernels Based on Natural Image Relations
The Journal of Machine Learning Research
Edge structure preserving image denoising
Signal Processing
Journal of Mathematical Imaging and Vision
Image denoising with anisotropic bivariate shrinkage
Signal Processing
Deconvolving Poissonian images by a novel hybrid variational model
Journal of Visual Communication and Image Representation
A closed form algorithm for superresolution
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Blind separation of sparse sources using jeffrey’s inverse prior and the EM algorithm
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Semi-supervised wavelet shrinkage
Computational Statistics & Data Analysis
International Journal of Speech Technology
Lapped transform-based image denoising with the generalised Gaussian prior
International Journal of Computational Vision and Robotics
Hi-index | 0.02 |
The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a wavelet-based denoising technique without any free parameters; it is, in this sense, a “universal” method. Our approach uses empirical Bayes estimation based on a Jeffreys' noninformative prior; it is a step toward objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed nonlinear shrinkage/thresholding rule which performs better than other more computationally demanding methods