Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models

Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models

Abstract In this paper, we propose a new method for support detection and estimation of sparse and approximately sparse signals from compressed measurements. Using a double Laplace mixture model as the parametric representation of the signal coefficients, the problem is formulated as a weighted ℓ 1...

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Bibliographic Details
Main Authors: Chiara Ravazzi, Enrico Magli
Format: Article
Language:English
Published: SpringerOpen 2018-07-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Compressed sensing
Sparse recovery
Gaussian mixture models
MAP estimation
Mixture models
Reweighted ℓ 1 minimization
Online Access:http://link.springer.com/article/10.1186/s13634-018-0565-5

Internet

http://link.springer.com/article/10.1186/s13634-018-0565-5

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