On the stability of the basis pursuit in the presence of noise
Signal Processing - Sparse approximations in signal and image processing
Breakdown of equivalence between the minimal l1-norm solution and the sparsest solution
Signal Processing - Sparse approximations in signal and image processing
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Digital Signal Processing
Uncertainty principles, extractors, and explicit embeddings of l2 into l1
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Applied Signal Processing
Sparse representations are most likely to be the sparsest possible
EURASIP Journal on Applied Signal Processing
An Efficient K-Hyperplane Clustering Algorithm and Its Application to Sparse Component Analysis
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Algorithm 890: Sparco: A Testing Framework for Sparse Reconstruction
ACM Transactions on Mathematical Software (TOMS)
Morphological Diversity and Sparsity for Multichannel Data Restoration
Journal of Mathematical Imaging and Vision
On the reconstruction of block-sparse signals with an optimal number of measurements
IEEE Transactions on Signal Processing
Dictionary learning for sparse approximations with the majorization method
IEEE Transactions on Signal Processing
High-resolution radar via compressed sensing
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Hierarchical Bayesian sparse image reconstruction with application to MRFM
IEEE Transactions on Image Processing
Comparing measures of sparsity
IEEE Transactions on Information Theory
Further results on stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Restricted isometry constants where lpsparse recovery can fail for 0
IEEE Transactions on Information Theory
l2/l1-optimization and its strong thresholds in approximately block-sparse compressed sensing
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Explicit thresholds for approximately sparse compressed sensing via l1-optimization
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Compressive cooperative sensing and mapping in mobile networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Uncertainty relations for shift-invariant analog signals
IEEE Transactions on Information Theory
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
Parametric dictionary design for sparse coding
IEEE Transactions on Signal Processing
Adaptive compressed sensing of speech signal based on data-driven dictionary
APCC'09 Proceedings of the 15th Asia-Pacific conference on Communications
Bayesian orthogonal component analysis for sparse representation
IEEE Transactions on Signal Processing
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
Theoretical and empirical results for recovery from multiple measurements
IEEE Transactions on Information Theory
Concave programming for minimizing the zero-norm over polyhedral sets
Computational Optimization and Applications
On the statistics of matching pursuit angles
Signal Processing
Stable recovery of sparse signals and an oracle inequality
IEEE Transactions on Information Theory
Dictionary identification: sparse matrix-factorization via l1-minimization
IEEE Transactions on Information Theory
Two conditions for equivalence of 0-norm solution and 1-norm solution in sparse representation
IEEE Transactions on Neural Networks
On the stable recovery of the sparsest overcomplete representations in presence of noise
IEEE Transactions on Signal Processing
The Gelfand widths of lp-balls for 0
Journal of Complexity
Optimized projection matrix for compressive sensing
EURASIP Journal on Advances in Signal Processing
Sparse representations and approximation theory
Journal of Approximation Theory
Improved stability conditions of BOGA for noisy block-sparse signals
Signal Processing
Full length article: On performance of greedy algorithms
Journal of Approximation Theory
Multi-label classification for image annotation via sparse similarity voting
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
SIAM Journal on Optimization
Efficient cross-correlation via sparse representation in sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization
SIAM Journal on Optimization
Strengthening hash families and compressive sensing
Journal of Discrete Algorithms
Restricted p---isometry property and its application for nonconvex compressive sensing
Advances in Computational Mathematics
Saliency-guided compressive sensing approach to efficient laser range measurement
Journal of Visual Communication and Image Representation
Multi-resolutive sparse approximations of d-dimensional data
Computer Vision and Image Understanding
Guarantees of augmented trace norm models in tensor recovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices
Journal of Mathematical Imaging and Vision
Non-negative sparse decomposition based on constrained smoothed ℓ0 norm
Signal Processing
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The purpose of this correspondence is to generalize a result by Donoho and Huo and Elad and Bruckstein on sparse representations of signals in a union of two orthonormal bases for RN. We consider general (redundant) dictionaries for RN, and derive sufficient conditions for having unique sparse representations of signals in such dictionaries. The special case where the dictionary is given by the union of L≥2 orthonormal bases for RN is studied in more detail. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of the ℓ0 optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may be highly redundant.