Publications associées à Unified Theory for Recovery of Sparse Signals in a General Transform Domain

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Dictionary Identifiability from Few Training Samples

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

ell^1

minimisation, or more precisely the problem of identifying a dictionary

dico

from a set of training samples

Y

knowing that $ ...

2008

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

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Conditions for recovery of sparse signals correlated by local transforms

Distributed Sensing of Signals Linked by Sparse Filtering

On Sampling Lattices with Similarity Scaling Relationships

The scale axis transform

Enhancement of extended depth of field by digital focusing

Sampling Bounds for Sparse Support Recovery in the Presence of Noise

Average case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence

Sparse signal recovery using Markov random fields

Distributed Compressed Sensing: Sparsity Models and Reconstruction Algorithms Using Annihilating Filter

Dictionary Identifiability from Few Training Samples

Average case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence

Conditions for recovery of sparse signals correlated by local transforms

Distributed Sensing of Signals Linked by Sparse Filtering

Sampling Bounds for Sparse Support Recovery in the Presence of Noise

Enhancement of extended depth of field by digital focusing

The scale axis transform

Sparse signal recovery using Markov random fields

On Sampling Lattices with Similarity Scaling Relationships

Distributed Compressed Sensing: Sparsity Models and Reconstruction Algorithms Using Annihilating Filter

Dictionary Identifiability from Few Training Samples