Results 41 to 50 of about 1,531 (279)
A Hierarchical Bayesian Model for Frame Representation [PDF]
, 2010 In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame
Amel Benazza-Benyahia +9 more
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Multi‐contrast reconstruction with Bayesian compressed sensing [PDF]
Magnetic Resonance in Medicine, 2011 AbstractClinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k‐space data with higher fidelity than when the ...
Bilgic, Berkin +2 more
openaire +4 more sources
Fast monostatic scattering analysis based on Bayesian compressive sensing [PDF]
, 2017 In conjugation with the method of moments, the Bayesian compressive sensing algorithm is utilized to fast analyze the monostatic electromagnetic scattering problem.
Wei E. I. Sha +5 more
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Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing [PDF]
, 2023 Multipath time-delay estimation is commonly encountered in radar and sonar signal processing. In some real-life environments, impulse noise is ubiquitous and significantly degrades estimation performance.
Ji, Xingyu, Cheng, Lei, Zhao, Hangfang
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Frequency-difference sparse Bayesian learning for unambiguous direction-of-arrival estimation [PDF]
JASA Express LettersThe frequency-difference (FD) method uses the FD Hadamard product, comprising auto-products to model below-band acoustic fields and unintended cross-products, for efficient direction-of-arrival (DOA) estimation under spatial aliasing.
Ze Yuan +3 more
doaj +1 more source
Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
core +1 more source
Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
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New Directions In Sparse Sampling and Estimation For Underdetermined Systems [PDF]
, 2013 A central objective in signal processing is to infer meaningful information from a set of measurements or data. While most signal models have an overdetermined structure (the number of unknowns less than the number of equations), traditionally very few ...
Piya Pal, Pal, Piya
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Robust Bayesian compressed sensing with outliers
Signal Processing, 2017 Abstract We consider the problem of robust compressed sensing where the objective is to recover a high-dimensional sparse signal from compressed measurements partially corrupted by outliers. A new sparse Bayesian learning method is developed for this purpose.
Qian Wan 0003 +4 more
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Bayesian Compressed Sensing with Heterogeneous Side Information [PDF]
2016 Data Compression Conference (DCC), 2016 The classical compressed sensing (CS) paradigm can be modified so as to leverage a signal correlated to the signal of interest, called side information, which is assumed to be provided a priori at the decoder in order to aid reconstruction. In this work, we propose a novel CS reconstruction method based on belief propagation principles, which manages ...
Evangelos Zimos +3 more
openaire +2 more sources