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IEEE Transactions on Signal Processing, 2008
The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B.
Lawrence Carin, Shihao Ji, Ya Xue
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The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B.
Lawrence Carin, Shihao Ji, Ya Xue
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Bayesian Compressive Sensing Using Laplace Priors
IEEE Transactions on Image Processing, 2010In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the sparsity of the unknown signal.
Aggelos K Katsaggelos +2 more
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Bayesian Compressive Sensing for clustered sparse signals [PDF]
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is introduced in this paper in order to investigate a class of structural sparse signals, called clustered sparse signals.
Yu, Lei +3 more
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Augmented Bayesian Compressive Sensing
2015 Data Compression Conference, 2015The simultaneous sparse approximation problem is concerned with recovering a set of multichannel signals that share a common support pattern using incomplete or compressive measurements. Multichannel modifications of greedy algorithms like orthogonal matching pursuit (OMP), as well as convex mixed-norm extensions of the Lasso, have typically been ...
David P. Wipf, Jeong-Min Yun, Qing Ling
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Variational Bayesian dynamic compressive sensing
2016 IEEE International Symposium on Information Theory (ISIT), 2016Dynamic compressed sensing (DCS) has recently gained popularity as a successful approach to recovering dynamic sparse signals. In this paper, we attack the problem from a Bayesian perspective. The proposed model imposes sparse constraints on both the unknown sparse signal and its temporal innovation via t priors. Due to the conjugacy between the priors
Hongwei Wang 0005 +4 more
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Clustered Compressed Sensing via Bayesian Framework
2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), 2015This paper provides clustered compressive sensing (CCS) based signal processing using Bayesian framework. Images like magnetic resonanse images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. Compressed sensing (CS) paradigm can be applied in order to boost such signal recoveries.
Solomon Tesfamicael, Faraz Barzideh
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Bit-plane compressive sensing with Bayesian decoding for lossy compression
28th Picture Coding Symposium, 2010This paper addresses the problem of reconstructing a com-pressively sampled sparse signal from its lossy and possibly insufficient measurements. The process involves estimations of sparsity pattern and sparse representation, for which we derived a vector estimator based on the Maximum a Posteriori Probability (MAP) rule.
Sz-Hsien Wu +2 more
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DOA Estimation Based on Bayesian Compressive Sensing
2019In this paper, Bayesian Compressive Sensing algorithm is studied. To deal with signals with multiple snapshots, we extend traditional Bayesian algorithm under the condition of single snapshot to multi-snapshot Bayesian Compressed Sensing (MBCS) algorithm and apply MBCS algorithm to direction of arrival (DOA) estimation of narrowband signals and ...
Suhang Li +3 more
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Synthesis of planar arrays through Bayesian Compressive Sensing
Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation, 2012The synthesis of sparse planar arrays matching a desired pattern is addressed by means of an innovative compressive sensing technique, namely the Bayesian Compressive Sensing (BCS). A relevance vector machine (RVM) is employed for computing the solution of the design problem formulated in a probabilistic fashion. A set of preliminary synthesis examples
Oliveri, Giacomo +2 more
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