Finding a sparse representation of a possibly noisy signal is a common problem in signal representation and processing. It can be modeled as a variational minimization with $ell_ au$-sparsity constraints for $ au<1$. Applications whose computation time is crucial require fast algorithms for this minimization. However, there are no fast methods for finding the exact minimizer, and to circumvent this limitation, we consider minimization up to a constant factor. We verify that arbitrary shrinkage rules provide closed formulas for such minimizers, and we introduce a new shrinkage strategy, which is adapted to $ au<1$
This thesis is a contribution to the field "equivalences of different methods of mathematical image ...
International audienceThis work addresses the properties of a sub-class of sigmoid based shrinkage f...
We study the connections between discrete 1-D schemes for non-linear diffusion and shift-invariant H...
Finding a sparse representation of a possibly noisy signal is a common problem in signal representa...
Finding a sparse representation of a possibly noisy signal can be modeled as a variational minimizat...
Finding a sparse representation of a noisy signal can be modeled as a variational minimization with ...
This paper examines the relationship between wavelet-based image processing algorithms and variation...
This paper examines the relationship between wavelet-based image processing algorithms and variation...
International audienceWavelet transforms are said to be sparse in that they represent smooth andpiec...
International audienceThis work addresses the unification of some basic functions and thresholds use...
The use of sparsity in signal processing frequently calls for the solution to the minimization probl...
In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the...
AbstractWe consider the abstract problem of approximating a function ψ0∈L1(Rd)∩L2(Rd) given only noi...
This thesis is a contribution to the field equivalences of different methods of mathematical image ...
AbstractWavelet shrinkage estimators are obtained by applying a shrinkage rule on the empirical wave...
This thesis is a contribution to the field "equivalences of different methods of mathematical image ...
International audienceThis work addresses the properties of a sub-class of sigmoid based shrinkage f...
We study the connections between discrete 1-D schemes for non-linear diffusion and shift-invariant H...
Finding a sparse representation of a possibly noisy signal is a common problem in signal representa...
Finding a sparse representation of a possibly noisy signal can be modeled as a variational minimizat...
Finding a sparse representation of a noisy signal can be modeled as a variational minimization with ...
This paper examines the relationship between wavelet-based image processing algorithms and variation...
This paper examines the relationship between wavelet-based image processing algorithms and variation...
International audienceWavelet transforms are said to be sparse in that they represent smooth andpiec...
International audienceThis work addresses the unification of some basic functions and thresholds use...
The use of sparsity in signal processing frequently calls for the solution to the minimization probl...
In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the...
AbstractWe consider the abstract problem of approximating a function ψ0∈L1(Rd)∩L2(Rd) given only noi...
This thesis is a contribution to the field equivalences of different methods of mathematical image ...
AbstractWavelet shrinkage estimators are obtained by applying a shrinkage rule on the empirical wave...
This thesis is a contribution to the field "equivalences of different methods of mathematical image ...
International audienceThis work addresses the properties of a sub-class of sigmoid based shrinkage f...
We study the connections between discrete 1-D schemes for non-linear diffusion and shift-invariant H...