Near-Optimal Signal Recovery from Random Projections¶
Candes, E. J., & Tao, T. (2006). Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies?. IEEE Transactions on Information Theory, 52(12), 5406-5425.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Sparse Coding
- In compressed sensing, a signal that is sparse in some known basis can be recovered from far fewer measurements than the Nyquist rate requires, making sparsity the structural prior that makes underdetermined inverse problems solvable.
This sourceCompressed-sensing result establishing that signals sparse in some basis can be recovered from far fewer measurements than the Nyquist rate requires — sparsity as the prior that makes underdetermined inverse problems solvable.
- In compressed sensing, a signal that is sparse in some known basis can be recovered from far fewer measurements than the Nyquist rate requires, making sparsity the structural prior that makes underdetermined inverse problems solvable.
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