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On the Compressed Measurements over Finite Fields: Sparse or Dense Sampling
[article]
2012
arXiv
pre-print
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One of interesting conclusions includes that unless the signal is "ultra" sparse, the sensing matrices do not have to be dense. ...
We consider compressed sampling over finite fields and investigate the number of compressed measurements needed for successful L0 recovery. ...
Draper and Malekpour [3] reported on the error exponents for recovery of sparse signals using uniform random sensing matrices over finite fields. Tan et al. have extended the works of ...
arXiv:1211.5207v1
fatcat:7c6cipsm6bca5pf7beix3f5nl4
Sparse Signal Recovery and Acquisition with Graphical Models
2010
IEEE Signal Processing Magazine
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as the sparse signal recovery. ...
An application du jour of the sparse signal recovery problem is compressive sensing (CS), which integrates the sparse representations with two other key aspects of the linear dimensionality reduction: ...
camera (bo cluster spatially on a Markov random field. ...
doi:10.1109/msp.2010.938029
fatcat:6bopdzqr4bda3dzpaof2tqnqlq
From variable density sampling to continuous sampling using Markov chains
[article]
2013
arXiv
pre-print
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To this end, we propose random walk continuous sampling based on Markov chains and we compare the reconstruction quality of this scheme to the state- of-the art. ...
., random radial or spiral sampling to achieve smoothness k-space trajectory). ...
The authors have shown that for a given s-sparse signal, the number of measurements needed to ensure its perfect recovery is O(s log(n)). ...
arXiv:1307.6960v1
fatcat:krmncafwjzgzdcqmjv6b2qvjwe
From Variable Density Sampling To Continuous Sampling Using Markov Chains
2013
Zenodo
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The authors have shown that for a given s-sparse signal, the number of measurements needed to ensure its perfect recovery is O(s log(n)). ...
We will show that this criterion makes it possible to obtain theoritical guarantees on the number of measurements necessary to reconstruct s sparse signals, using variable density sampling or markovian ...
doi:10.5281/zenodo.54378
fatcat:pyolb5osifggvnyj7nnrv7jpq4
Compressive Sensing
[chapter]
2020
Computer Vision
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Another idea is in the use of non-sparse models such as Markov random fields [18] for CSwith the eventual goal of using the Ising model for sensing images and background subtracted silhouettes. ...
The guarantees on the recovery of signals extend to the case when s is not exactly sparse but compressible. ...
doi:10.1007/978-3-030-03243-2_647-1
fatcat:g6yiupnxpve7zgvzy2fnglaaxu
Brain Tumor Segmentation from Multispectral MRIs Using Sparse Representation Classification and Markov Random Field Regularization
2015
International Journal of Signal Processing, Image Processing and Pattern Recognition
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At last, the Markov random field (MRF) regularization introduces spatial constraints to the SRC to take into account the pair-wise homogeneity in terms of classification labels and multispectral voxel ...
Then, the sparse representation classification (SRC) is applied to classify the brain tumor and normal brain tissue in the whole image. ...
Acknowledgements This paper is a revised and expanded version of a paper entitled "Brain Tumor Segmentation from multispectral MRIs Using Sparse Representation Classification and Markov Random Field Regularization ...
doi:10.14257/ijsip.2015.8.9.24
fatcat:mf6ri6hy5rhqjb4w4tdsqblrl4
Underwater acoustic channel estimation based on sparse recovery algorithms
2011
IET Signal Processing
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The authors then propose two enhancements to the sparse recovery-based UWA channel estimator by exploiting the UWA channel temporal correlations, including the use of a first-order Gauss -Markov model ...
The authors consider underwater acoustic (UWA) channel estimation based on sparse recovery using the recently developed homotopy algorithm. ...
sparse recovery algorithms. ...
doi:10.1049/iet-spr.2010.0347
fatcat:7k2ozikdgffydffmuxl7fbcwge
Distributed least mean squares strategies for sparsity-aware estimation over Gaussian Markov random fields
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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The measurements collected at different nodes are assumed to be spatially correlated and distributed according to a Gaussian Markov random field (GMRF) model. ...
Simulation results show the potential advantages of the proposed strategies for online recovery of sparse vectors. ...
GAUSSIAN MARKOV RANDOM FIELDS A Markov random field is represented through an undirected graph [15] . ...
doi:10.1109/icassp.2014.6854649
dblp:conf/icassp/LorenzoB14
fatcat:jhvvydt3obashox3ozzixxkhke
2019 Index IEEE Transactions on Computational Imaging Vol. 5
2019
IEEE Transactions on Computational Imaging
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., +, TCI March 2019 17-26
Manifold Recovery Using Kernel Low-Rank Regularization: Application to
Dynamic Imaging. ...
., +, TCI June 2019 317-332
Manifold Recovery Using Kernel Low-Rank Regularization: Application to
Dynamic Imaging. ...
doi:10.1109/tci.2019.2959176
fatcat:g7nuyesverg2xbjwbzuyp6ovyy
Variance State Propagation for Structured Sparse Bayesian Learning
[article]
2019
arXiv
pre-print
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Markov random field (MRF) is introduced to characterize the state of the variances of the Gaussian priors. ...
Simulation results demonstrate that the VSP algorithm is able to handle a variety of block-sparse signal recovery tasks and presents a significant advantage over the existing methods. ...
a Markov random field (MRF) prior to describe the block-sparse structure of v. ...
arXiv:1910.07352v1
fatcat:eclye3fptjeszjmkfpn62jdo5e
Functional MRI Analysis with Sparse Models
[chapter]
2013
Lecture Notes in Computer Science
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In this paper, we summarize our recent work on sparse models, including both sparse regression and sparse Gaussian Markov Random Fields (GMRF), in neuroimaging applications, such as functional MRI data ...
Sparse models embed variable selection into model learning (e.g., by using l1-norm regularizer). ...
Furthermore, we investigate structural differences of sparse Gaussian Markov Random Fields, or GMRFs, constructed from fMRI data via l 1 -regularized maximum likelihood (inverse covariance estimation). ...
doi:10.1007/978-3-642-40994-3_43
fatcat:ndhgvryfljhjrn5ajp6ut3fvlq
Adaptive structured recovery of compressive sensing via piecewise autoregressive modeling
2010
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
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to adapt to changing second order statistics of a signal in CS recovery. ...
Given the nonstationarity of many natural signals such as images, the sparse space varies in time/spatial domain. ...
Since an image is a random Markov field (RMF) of a modest order, each row vector a n is sparse, i.e., only a very small portion of the elements of a n are nonzero. ...
doi:10.1109/icassp.2010.5495811
dblp:conf/icassp/WuZ10
fatcat:u5zryxxefvb3pg6fs66xkovpv4
Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior
2020
Sensors
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However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. ...
By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. ...
In fact, the block sparse structure in the support matrix is captured by the Markov random field. The nonzero coefficients of recovered speech appeared in clusters. ...
doi:10.3390/s20164609
pmid:32824410
fatcat:xozerg6zhfee3kbumyil2r2efe
Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective
2010
Proceedings of the IEEE
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We consider sparse and compressible signal models for deterministic and random signals, structured sparse and compressible signal models, point clouds, and manifold signal models. ...
As a bonus, we point out a common misconception related to probabilistic compressible signal models, namely, by showing that the oft-used generalized Gaussian and Laplacian models do not support stable ...
The classical Ising Markov random field model capturing how the significant image pixels cluster together has been applied to background subtracted image recovery [51] .
D. ...
doi:10.1109/jproc.2009.2038076
fatcat:boqfqi4b2vb6dn7kuqc6e7rb4a
Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure
2011
Mathematical Problems in Engineering
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Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved ...
The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. ...
CS exploits the sparse structure in signal, and it enables signal reconstruction from a small number of random samples. ...
doi:10.1155/2011/606974
fatcat:fdtvyonpfvgmxf45q6olu2hcuq
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