AbstractThis paper concerns systems with small coherence parameter. Simple greedy-type algorithms perform well on these systems, which are also useful in the construction of compressed sensing matrices.We discuss the following problems for both Rn and Cn. How large can a dictionary be, if we prescribe the coherence parameter? How small could the resulting coherence parameter be, if we impose a size on the dictionary? How could we construct such a system? Several fundamental results from different areas of mathematics shed light on these important problems with far-reaching implications in approximation theory
AbstractThe compressed sensing problem for redundant dictionaries aims to use a small number of line...
In an incoherent dictionary, most signals that admit a sparse representation admit a unique sparse r...
The well-known shrinkage technique is still relevant for contemporary signal processing problems ove...
AbstractThis paper concerns systems with small coherence parameter. Simple greedy-type algorithms pe...
This article presents novel results concerning the recovery of signals from undersampled data in the...
This article presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching ...
Compressive sampling (CoSa) has provided many methods for signal recovery of signals compressible wi...
AbstractThis article presents novel results concerning the recovery of signals from undersampled dat...
AbstractA major enterprise in compressed sensing and sparse approximation is the design and analysis...
Optimizing the mutual coherence of a learned dictionary plays an important role in sparse representa...
This article presents novel results concerning the recovery of signals from undersampled data in the...
Consider the problem of recovering an unknown signal from undersampled measurements, given the knowl...
Dictionary learning problem has become an active topic for decades. Most existing learning methods t...
International audienceTen years ago, Mallat and Zhang proposed the Matching Pursuit algorithm : sinc...
In this paper we define a new coherence index, named 2-coherence, of a given dictionary and study it...
AbstractThe compressed sensing problem for redundant dictionaries aims to use a small number of line...
In an incoherent dictionary, most signals that admit a sparse representation admit a unique sparse r...
The well-known shrinkage technique is still relevant for contemporary signal processing problems ove...
AbstractThis paper concerns systems with small coherence parameter. Simple greedy-type algorithms pe...
This article presents novel results concerning the recovery of signals from undersampled data in the...
This article presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching ...
Compressive sampling (CoSa) has provided many methods for signal recovery of signals compressible wi...
AbstractThis article presents novel results concerning the recovery of signals from undersampled dat...
AbstractA major enterprise in compressed sensing and sparse approximation is the design and analysis...
Optimizing the mutual coherence of a learned dictionary plays an important role in sparse representa...
This article presents novel results concerning the recovery of signals from undersampled data in the...
Consider the problem of recovering an unknown signal from undersampled measurements, given the knowl...
Dictionary learning problem has become an active topic for decades. Most existing learning methods t...
International audienceTen years ago, Mallat and Zhang proposed the Matching Pursuit algorithm : sinc...
In this paper we define a new coherence index, named 2-coherence, of a given dictionary and study it...
AbstractThe compressed sensing problem for redundant dictionaries aims to use a small number of line...
In an incoherent dictionary, most signals that admit a sparse representation admit a unique sparse r...
The well-known shrinkage technique is still relevant for contemporary signal processing problems ove...