Name
Abstract | ||
|---|---|---|
Motivated by the observation that a given signal x admits sparse representations in multiple dictionaries Ψd but with varying levels of sparsity across dictionaries, we propose two new algorithms for the reconstruction of (approximately) sparse signals from noisy linear measurements. Our first algorithm, Co-L1, extends the well-known lasso algorithm from the L1 regularizer ∥Ψx∥1 to composite regul... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCI.2015.2485078 | IEEE Transactions on Computational Imaging |
Keywords | Field | DocType |
AWGN,Optimization,Approximation algorithms,Bayes methods,Image reconstruction,Inference algorithms,Convergence | Discrete mathematics,Mathematical optimization,Lasso (statistics),Regularization (mathematics),Map inference,Mathematics,Lambda,Bayesian probability | Journal |
Volume | Issue | ISSN |
1 | 4 | 2573-0436 |
Citations | PageRank | References |
3 | 0.38 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rizwan Ahmad | 1 | 3 | 1.06 |
| Philip Schniter | 2 | 1620 | 93.74 |