Bayesian Compressive Sensing Using Laplace Priors

By S. D. Babacan, Rafael Molina, Aggelos K. Katsaggelos. IEEE Transactions on Image Processing, 2010

798 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Chaotic Time Series Prediction Based on a Novel Robust Echo State Network (2012, background): “They include providing probabilistic prediction and automatic estimation of model parameters [22], [23].”
Compressive Sensing in Electromagnetics - A Review (2015, methodology): “In addition, BCS approaches [9] have been investigated for solving the perturbed CS problem [68].”
Off-grid Channel Estimation with Sparse Bayesian Learning for OTFS Systems (2021, background): “The hyperprior formulation defined in (31) and (32) results in a Laplace prior, see (17) in [21], and hence a sparse estimation of h is preferred.”
Sparse Methods for Direction-of-Arrival Estimation (2016, result): “ that with other sparse priors for xthat may possess different sparsity promoting properties, the obtained objective function of SBL can be slightly different from that of the MLE in (31) (see, e.g., [67]). 3.2 Sparse Representation and DOA Estimation: the Link and the Gap In this subsection we disc”
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference (2011, methodology): “As an example, a Laplace signal prior leads to a maximum a posteriori (MAP) optimal estimate that coincides with an optimal solution to the optimization[10].”
Exploiting sparsity and co-occurrence structure for action unit recognition (2015, methodology): “Compressed sensing is perhaps one of the more promising methods [13], [1], [16].”