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Compressed sensing with structured sparsity and structured acquisition
[article]
2016
arXiv
pre-print
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In this paper, we derive new CS results for structured acquisitions and signals satisfying a prior structured sparsity. ...
Structured acquisition with blocks of measurements are easy to implement, and provide good reconstruction results in practice. ...
Acknowledgement The authors would like to thank Ben Adcock and Anders Hansen for their availibility for discussion. They are also grateful to Nicolas Chauffert for discussion. ...
arXiv:1505.01619v2
fatcat:nn3t6yaxynhlxhk355m5a5qdga
Compressed sensing with structured sparsity and structured acquisition
2017
Applied and Computational Harmonic Analysis
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Structured acquisition with blocks of measurements are easy to implement, and they give good reconstruction results in practice [?]. ...
In this work, we fill the gap between CS theory and acquisitions made in practice. To this end, the key feature to consider is the structured sparsity of the signal to reconstruct. ...
Références [1] Lustig, Michael and Donoho, David and Pauly, John M., Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic resonance in medicine, 2007. ...
doi:10.1016/j.acha.2017.05.005
fatcat:rvpk7c76drb3bfdqkkdk26hr6a
Channel Estimation in OFDM System using Compressive Sensing Framework: A Review
2020
Helix
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Spectral efficiency can be considerably improved with channel estimation exploiting compressive sensing. ...
Paper addresses Channel Estimation by means of Compressive Sensing (CS). Here system under consideration is OFDM. Paper reviews the emerging techniques of pilot design and allocation schemes. ...
Doubly selective
CE based on
SCS [15]
Table No 1 No : Channel Estimation with Structured Compressive Scheme
Table No : No 2 depicts Channel Estimation exploiting Compressive Sensing with Low-Complexity ...
doi:10.29042/2020-10-2-53-57
fatcat:nphycxvgtralfotmtaoqb4h5ie
Distributed compressed video sensing
2009
2009 16th IEEE International Conference on Image Processing (ICIP)
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Index Termsdistributed video coding, Wyner-Ziv coding, compressed sensing, compressive sensing, sparse recovery with decoder side information, structurally random matrices. ...
However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparse recovery with side information. ...
compensation; • Sparse recovery with decoder side information; • A practical, real-time system design of distributed video coding based on a compressed sensing acquisition method of both local block-based ...
doi:10.1109/icip.2009.5414631
dblp:conf/icip/DoCNNGT09
fatcat:sspdrzv66jgvnbr6z45lczmoxa
Distributed Compressed Video Sensing
2009
2009 43rd Annual Conference on Information Sciences and Systems
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Index Termsdistributed video coding, Wyner-Ziv coding, compressed sensing, compressive sensing, sparse recovery with decoder side information, structurally random matrices. ...
However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparse recovery with side information. ...
compensation; • Sparse recovery with decoder side information; • A practical, real-time system design of distributed video coding based on a compressed sensing acquisition method of both local block-based ...
doi:10.1109/ciss.2009.5054678
dblp:conf/ciss/DoCNNGT09
fatcat:keroyuxwwjdwxb3ekyw2p5gbxe
Best Basis Compressed Sensing
2010
IEEE Transactions on Signal Processing
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Instead of regularizing the compressed sensing inverse problem with a sparsity prior in a fixed basis, our framework makes use of sparsity in a tree-structured dictionary of orthogonal bases. ...
Numerical experiments on sounds and geometrical images indeed show that this best basis search improves the recovery with respect to fixed sparsity priors. ...
COMPRESSED SENSING
1) Compressed Sensing Acquisition and Recovery: Compressed sensing acquisition computes a fixed set of linear measurements of an unknown high resolution signal with with The price ...
doi:10.1109/tsp.2010.2042490
fatcat:kic65en6kzdjbahnm242eujzl4
Evaluation of Primal-Dual Splitting Algorithm for MRI Reconstruction Using Spatio-Temporal Structure Tensor and L1-2 Norm
2020
Makara Journal of Technology
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The algorithm is compared with PDSbased algorithm using conventional regularizations, i.e., wavelet sparsity and total variation. ...
Abstrak Rekonstruksi Compressive Sensing MRI menggunakan Spatial-Temporal Structure Tensor melalui Metode Primal-Dual Splitting. ...
To evaluate the performance of our proposed algorithm, we compare our results with conventional regularizations, i.e., wavelet sparsity and total variation, used with compressive sensing approach solved ...
doi:10.7454/mst.v23i3.3892
fatcat:aprxptb3gzh3fagr5p3pqxquvu
Overcomplete compressed sensing of ray space for generating free viewpoint images
2013
2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
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In this paper, we focus on compressed sensing to sparsely capture a ray space at encoder and reconstruct it at decoder. ...
sensed data. ...
Traditionally, compressed sensing [7] , [8] enables a sparse signal to be recovered from fewer nonadaptive and linear measurements by certain optimization tool with sparsity promotion. ...
doi:10.1109/apsipa.2013.6694266
dblp:conf/apsipa/YaoTF13
fatcat:nr56stm2gnbb5glwqcl4iskamm
A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks
2019
2019 IEEE International Smart Cities Conference (ISC2)
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better and faster results using the sparse structure of the radio spectrum. ...
Therefore, this paper represents an in-depth survey of the best requirements of compressive sensing and spectrum sensing techniques for robust combination and effective solution for wideband cognitive ...
In the second category, deterministic matrices are structured matrices that allow faster acquisition with less memory storage. ...
doi:10.1109/isc246665.2019.9071710
dblp:conf/isc2/BenazzouzaRSH19
fatcat:e6oemvwmwzem7kkq7dni62hz6u
Sparse Representation for Wireless Communications: A Compressive Sensing Approach
2018
IEEE Signal Processing Magazine
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2018 pre-print arXiv:1801.08206v1 fatcat:fgvd7rk2kraq7j5grey3th3zveOther Versions
In this article, we will discuss various applications of sparse representation in wireless communications, with focus on the most recent compressive sensing (CS) enabled approaches. ...
With the help of the sparsity property, CS is able to enhance the spectrum efficiency and energy efficiency for the fifth generation (5G) networks and Internet of Things (IoT) networks. ...
Traditionally, signal acquisition and transmission adopt the procedure with sampling and compression. ...
doi:10.1109/msp.2018.2789521
fatcat:wamvxn7kebavtjroau4ksfbwea
Compressed sensing for denoising in adaptive system identification
2012
20th Iranian Conference on Electrical Engineering (ICEE2012)
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We propose a new technique for adaptive identification of sparse systems based on the compressed sensing (CS) theory. ...
We manipulate the transmitted pilot (input signal) and the received signal such that the weights of adaptive filter approach the compressed version of the sparse system instead of the original system. ...
For this purpose, we deal with the time variant feature of compressed sensing measurements. ...
doi:10.1109/iraniancee.2012.6292545
fatcat:ya6pspgjrfgtrbw4c4bvie2aga
An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
2017
Wireless Communications and Mobile Computing
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CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. ...
Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). ...
Simulation results and discussion are presented in Section 4. Section 5 concludes the paper.
Compressed Sensing
Acquisition Model. ...
doi:10.1155/2017/9823684
fatcat:rulahtwrhvaqhk4xz7yrpmceve
[Paper] Compressed Sensing of Ray Space for Free Viewpoint Image (FVI) Generation
2014
ITE Transactions on Media Technology and Applications
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In this paper, we propose to adopt compressed sensing to sparsely sense and reconstruct a ray space. ...
Furthermore, since EPI presents unique structures, another dictionary which can represent this structure is also developed to take place of common orthonormal basis in compressed sensing procedure. ...
ACKNOWLEDGEMENT This work was supported by Grant-in-Aid for Scientific Research (C) with Number 24560450. ...
doi:10.3169/mta.2.23
fatcat:e2t4m5ngpzbqzkaa7ynl4xy2r4
Low power real-time data acquisition using compressive sensing
2017
Micro- and Nanotechnology Sensors, Systems, and Applications IX
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New possibilit ies exist for the development of novel hardware/software platforms havin g fast data acquisition capability with low power requirements. ...
With an in -memo ry design, the new co mpressive sensing based instrumentation performs dig itization only when there is enough variation in the input and when the random selection matrix chooses this ...
MODEL BASED COMPRESSIVE SENSING FOR EXPONENTIAL DECAY
What is compressive sensing? Let us begin with a brief review of co mpressive sensing theory [2, [4] [5] [6] [7] . ...
doi:10.1117/12.2263220
fatcat:v5xl2qi7tzfuhbfr6hgvr7oee4
Compressive sensing of a superposition of pulses
2010
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
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Compressive Sensing (CS) has emerged as a potentially viable technique for the efficient acquisition of high-resolution signals and images that have a sparse representation in a fixed basis. ...
Experiments with real world data demonstrate that our method provides considerable gains over standard state-of-the-art compressive sensing techniques in terms of numbers of measurements required for stable ...
CS can be viewed as a scheme for simultaneous sensing and compression; instead of being proportional to the Fourier bandwidth, the rate of data acquisition need only be proportional to the sparsity of ...
doi:10.1109/icassp.2010.5495801
dblp:conf/icassp/HegdeB10
fatcat:x7gryfljtfbzzmoaqx35u4n3ve
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