Results 11 to 20 of about 1,531 (279)
Bayesian compressive sensing for cluster structured sparse signals [PDF]
Signal Processing, 2012 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. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered ...
, J P Barbot
exaly +6 more sources
Video Compressive Sensing Reconstruction Algorithm Based on 3D Tree Structure and Bayesian Model [PDF]
Jisuanji gongcheng, 2016 In view of the traditional underwater video coding requiring higher underwater acoustic channel and the scenes of underwater video with uneven illumination being complex and not fixed,this paper presents a reconstruction algorithm with Three Dimension(3D)
ZHUANG Yanbin,WANG Zunzhi,XIAO Xianjian,ZHANG Xuewu
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Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion [PDF]
Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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Bayesian Compressive Sensing Wideband Spectrum Detection Based on Energy Efficiency [PDF]
Jisuanji gongcheng, 2016 In Cognitive Radio Network(CRN),wideband spectrum detection based on Compressive Sensing(CS) only focuses on spectral efficiency.Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the ...
WANG Zan,XU Xiaorong,YAO Yingbiao
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Bayesian Inference and Compressed Sensing [PDF]
, 2017 This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal processing. Among the recovery methods used in CS literature, the convex relaxation methods are reformulated again using the Bayesian framework and this method is applied in different CS applications such as magnetic resonance imaging (MRI), remote ...
Solomon A. Tesfamicael, Faraz Barzideh
core +8 more sources
Energy constraint Bayesian compressive sensing detection algorithm [PDF]
Tongxin xuebao, 2012 To solve the shortage of nodes handling ability and limited energy in wireless sensor network,an energy constraint Bayesian compressive sensing detection algorithm was proposed.To balance the energy of the whole network and prevent network paralyzed due ...
Chun-hui ZHAO, Yun-long XU
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Achievable performance of Bayesian compressive sensing based spectrum sensing [PDF]
2014 IEEE International Conference on Ultra-WideBand (ICUWB), 2014 In wideband spectrum sensing compressive sensing approaches have been used at the receiver side to decrease the sampling rate if the wideband signal can be represented as sparse in a given domain. While most studies consider the reconstruction of primary user's signal accurately it is indeed more important to analyze the presence or absence of the ...
Başaran, Mehmet +3 more
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Variational Bayesian algorithm for distributed compressive sensing [PDF]
2015 IEEE International Conference on Communications (ICC), 2015 Distributed compressive sensing (DCS) concerns the reconstruction of multiple sensor signals with reduced numbers of measurements, which exploits both intra- and inter-signal correlations. In this paper, we propose a novel Bayesian DCS algorithm based on variational Bayesian inference.
Wei Chen 0016, Ian J. Wassell
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Anti-noise variational sparse Bayesian estimation ghost imaging based on 3Level factor graph [PDF]
Scientific ReportsIn response to existing compressed sensing ghost imaging (CSGI) schemes, an innovative Bayesian compressed sensing ghost imaging with better anti-noise performance is proposed, by using the sparse representation of K-Singular Value Decomposition (KSVD ...
Siqing Xiang +9 more
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Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach [PDF]
Journal of Imaging, 2019 In this paper, the full-vectorial three-dimensional (3D) microwave imaging (MI) of sparse scatterers is dealt with. Towards this end, the inverse scattering (IS) problem is formulated within the contrast source inversion (CSI) framework and it is aimed ...
Marco Salucci +2 more
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