Results 31 to 40 of about 1,531 (279)
Complex multitask compressive sensing using Laplace priors
Electronics Letters, 2021 Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems.
Qilei Zhang, Zhen Dong, Yongsheng Zhang
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Complex multitask Bayesian compressive sensing [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values.
Qisong Wu +3 more
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Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior
IET Signal Processing, 2022 Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed.
Qilei Zhang, Lei Yu, Feng He, Yifei Ji
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Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning
Sensors, 2022 In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method.
Yanbin Zhang +4 more
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Bayesian compressed sensing in ultrasound imaging [PDF]
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 Following our previous study on compressed sensing for ultrasound imaging, this paper proposes to exploit the image sparsity in the frequency domain within a Bayesian approach. A Bernoulli-Gaussian prior is assigned to the Fourier transform of the ultrasound image in order to enforce sparsity and to reconstruct the image via Bayesian compressed sensing.
Celine Quinsac +4 more
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Electronics Letters, 2021 An approach based on the Green function and the Born approximation is used for impulsive radio ultra‐wideband microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions.
Nicolás Zilberstein +2 more
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Bayesian Compressive Sensing Via Belief Propagation [PDF]
IEEE Transactions on Signal Processing, 2010 Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear ...
Dror Baron +2 more
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Variational Bayesian Algorithm for Quantized Compressed Sensing [PDF]
IEEE Transactions on Signal Processing, 2013 Accepted by IEEE Trans. Signal Processing.
Zai Yang, Lihua Xie 0001, Cishen Zhang
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Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas [PDF]
, 2012 Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a ...
Nannini, Matteo +2 more
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Robust Bayesian Compressed sensing
CoRR, 2016 We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the outliers from sparse signal recovery.
Qian Wan 0003 +3 more
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