An Introduction To Compressive Sampling
Abstract
Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Non-equilibrium exchanges between system and environment may lead to generation of long-lived quantum coherence in the system.
“This and other non-equilibrium exchanges between system and environment can have considerable influence on the system dynamics [28-35], influence the direction of energy transfer [28] and may even lead to the generation of long-lived quantum coherence in the system [28, 36-38].”
From Vibrations, quanta and biology (2013) · cites this paper as [36]
Inclusion of significant coupling of electronic motion to long-lived vibrational modes is capable of explaining the long-lived coherences observed in ultrafast 2-D spectroscopy on photosynthetic complexes.
“In the following we will show that the inclusion of significant coupling of electronic motion to long-lived vibrational modes [28, 34] are capable of explaining the observations [36, 37]”
From Vibrations, quanta and biology (2013) · cites this paper as [36]
Electronic coherence may emerge from transiently exciting robust, weakly dephasing vibrational coherences which then transfer back coherence to those exciton transitions that are well matched to the mode.
“The physical picture presented here illustrates a key point: electronic coherence may emerge from transiently exciting robust, weakly dephasing vibrational coherences which then transfer back coherence to those exciton transitions that are well matched to the mode [28, 36, 37].”
From Vibrations, quanta and biology (2013) · cites this paper as [36]
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994 · see citing works
Donoho, D. L. (2004) · Cited by 17,049 · see citing works
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639 · see citing works
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,828 · see citing works
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036 · see citing works
Decoding by Linear Programming
Candès, E. J., & Tao, T. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.858979 · Cited by 6,987 · see citing works
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candès, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,973 · see citing works
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,734 · see citing works
Book Review: JPEG2000: Image Compression Fundamentals, Standards and Practice
Rabbani, M. (2002) · Journal of Electronic Imaging · doi:10.1117/1.1469618 · Cited by 3,192 · see citing works
The Dantzig selector: Statistical estimation when p is much larger than n
Candès, E. J., & Tao, T. (2007) · The Annals of Statistics · doi:10.1214/009053606000001523 · Cited by 3,152 · see citing works
A Simple Proof of the Restricted Isometry Property for Random Matrices
Baraniuk, R. G., Davenport, M. A., DeVore, R., & Wakin, M. B. (2008) · Constructive Approximation · doi:10.1007/s00365-007-9003-x · Cited by 2,310 · see citing works
Self-citationSparsity and incoherence in compressive sampling
Candès, E. J., & Romberg, J. K. (2007) · Inverse Problems · doi:10.1088/0266-5611/23/3/008 · Cited by 2,109 · see citing works
Uncertainty principles and ideal atomic decomposition
Donoho, D. L., & Huo, X. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.959265 · Cited by 1,907 · see citing works
Sampling signals with finite rate of innovation
Vetterli, M., Marziliano, P., & Blu, T. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.1003065 · Cited by 1,071 · see citing works
Uncertainty Principles and Signal Recovery
Donoho, D. L., & Stark, P. B. (1989) · SIAM Journal on Applied Mathematics · doi:10.1137/0149053 · Cited by 1,054 · see citing works
Compressed sensing and best 𝑘-term approximation
Cohen, A., Dahmen, W., & DeVore, R. (2008) · Journal of the American Mathematical Society · doi:10.1090/s0894-0347-08-00610-3 · Cited by 1,048 · see citing works
On sparse reconstruction from Fourier and Gaussian measurements
Rudelson, M., & Vershynin, R. (2007) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20227 · Cited by 888 · see citing works
Robust Modeling With Erratic Data
Claerbout, J. F., & Muir, F. (1973) · Geophysics · doi:10.1190/1.1440378 · Cited by 838 · see citing works
Linear Inversion of Band-Limited Reflection Seismograms
Santosa, F., & Symes, W. W. (1986) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0907087 · Cited by 737 · see citing works
Algebraic Codes for Data Transmission
Blahut, R. E. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511800467 · Cited by 657 · see citing works
Signal Reconstruction From Noisy Random Projections
Haupt, J., & Nowak, R. D. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.880031 · Cited by 615 · see citing works
Uniform Uncertainty Principle for Bernoulli and Subgaussian Ensembles
Mendelson, S., Pajor, A., & Tomczak-Jaegermann, N. (2008) · Constructive Approximation · doi:10.1007/s00365-007-9005-8 · Cited by 325 · see citing works
Spectrum-blind minimum-rate sampling and reconstruction of multiband signals
Feng, P., & Bresler, Y. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1996.544131 · Cited by 284 · see citing works
Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements
Rudelson, M., & Vershynin, R. (2006) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2006.286463 · Cited by 234 · see citing works
Improved time bounds for near-optimal sparse Fourier representations
Gilbert, A. C., Muthukrishnan, S., & Strauss, M. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.615931 · Cited by 216 · see citing works
Coifman, R. R., Geshwind, F. B., & Meyer, Y. (2001) · Applied and Computational Harmonic Analysis · doi:10.1006/acha.2000.0313 · Cited by 171 · see citing works
Rapid MR Imaging with "Compressed Sensing" and Randomly Under-Sampled 3DFT Trajectories
Lustig, M. S., Donoho, D. L., & Pauly, J. M. (2004) · Cited by 59 · see citing works
A compressed sensing camera : New theory and an implementation using digital micromirrors
Takhar, D. (2006) · Medical Entomology and Zoology · Cited by 36 · see citing works
Flagged first, then by citations
3 of the 32 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517384113 · Cited by 4,796 · see citing works
A tutorial on synthetic aperture radar
Moreira, A., Prats, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2248301 · Cited by 2,866 · see citing works
Kuybeda, O., Frank, G. A., Bartesaghi, A., Borgnia, M. J., Subramaniam, S., & Sapiro, G. (2012) · Journal of Structural Biology · doi:10.1016/j.jsb.2012.10.010 · Cited by 2,200 · see citing works
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
Inverse problems: A Bayesian perspective
Stuart, A. M. (2010) · Acta Numerica · doi:10.1017/s0962492910000061 · Cited by 1,743 · see citing works
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817 · see citing works
Challenges of Big Data analysis
Fan, J., Han, F., & Liu, H. (2014) · National Science Review · doi:10.1093/nsr/nwt032 · Cited by 1,482 · see citing works
Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation
Renzo, M. D., Haas, H., Ghrayeb, A., Sugiura, S., & Hanzo, L. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2287851 · Cited by 1,445 · see citing works
Compressed Sensing Off the Grid
Tang, G., Bhaskar, B. N., Shah, P., & Recht, B. (2013) · IEEE Transactions on Information Theory · doi:10.1109/tit.2013.2277451 · Cited by 1,177 · see citing works
Data-Driven Science and Engineering
Brunton, S. L., & Kutz, J. N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108380690 · Cited by 1,177 · see citing works
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank RepresentationPreprint
Lin, Z., Liu, R., & Su, Z. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1109.0367 · Cited by 1,154 · see citing works
Structured Compressed Sensing: From Theory to Applications
Duarte, M. F., & Eldar, Y. C. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2161982 · Cited by 1,134 · see citing works
Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances
Axell, E., Leus, G. J. T., Larsson, E. G., & Poor, H. V. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2183771 · Cited by 1,120 · see citing works
A Survey of Sparse Representation: Algorithms and Applications
Zhang, Z., Xu, Y. D., Yang, J. J., Li, X., & Zhang, D. D. (2015) · IEEE Access · doi:10.1109/access.2015.2430359 · Cited by 1,088 · see citing works
Single disperser design for coded aperture snapshot spectral imaging
Wagadarikar, A. A., John, R., Willett, R. M., & Brady, D. J. (2008) · Applied Optics · doi:10.1364/ao.47.000b44 · Cited by 1,063 · see citing works
Katz, O., Bromberg, Y., & Silberberg, Y. R. (2009) · Applied Physics Letters · doi:10.1063/1.3238296 · Cited by 1,024 · see citing works
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.083802 · Cited by 944 · see citing works
Sensitivity to Basis Mismatch in Compressed Sensing
Chi, Y., Scharf, L. L., Pezeshki, A., & Calderbank, A. R. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2112650 · Cited by 926 · see citing works
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
Yang, Y., Sun, J. Z., Li, H., & Xu, Z. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2883941 · Cited by 913 · see citing works
Spectrum Sensing for Cognitive Radio
Haykin, S., Thomson, D. J., & Reed, J. H. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2015711 · Cited by 836 · see citing works
hp-VPINNs: Variational physics-informed neural networks with domain decomposition
Kharazmi, E., Zhang, Z., & Karniadakis, G. E. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113547 · Cited by 840 · see citing works
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., & Ashok, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.55 · Cited by 768 · see citing works
A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions
Zeng, Y., Liang, Y., Hoang, A. T., & Zhang, R. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2010/381465 · Cited by 742 · see citing works
Lensless computational imaging through deep learning
Sinha, A., Lee, J. W., Li, S., & Barbastathis, G. (2017) · Optica · doi:10.1364/optica.4.001117 · Cited by 710 · see citing works
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
Sarıyanidi, E., Güneş, H., & Cavallaro, A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2366127 · Cited by 689 · see citing works
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems
Rao, X., & Lau, V. K. N. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2324991 · Cited by 685 · see citing works
Efficient quantum state tomography
Cramer, M., Plenio, M. B., Flammia, S. T., Somma, R. D., Gross, D., Bartlett, S. D., Landon-Cardinal, O., Poulin, D., & Liu, Y.-K. (2010) · Nature Communications · doi:10.1038/ncomms1147 · Cited by 677 · see citing works
Compressive Coded Aperture Spectral Imaging: An Introduction
Arce, G. R., Brady, D. J., Carin, L., Argüello, H., & Kittle, D. S. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2278763 · Cited by 670 · see citing works
On the Role of Sparse and Redundant Representations in Image Processing
Elad, M., Figueiredo, M. A. T., & Ma, Y. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2037655 · Cited by 663 · see citing works
Single-pixel imaging 12 years on: a review
Gibson, G. M., Johnson, S. D., & Padgett, M. J. (2020) · Optics Express · doi:10.1364/oe.403195 · Cited by 658 · see citing works
Adaptive Restart for Accelerated Gradient Schemes
O’Donoghue, B., & Candès, E. J. (2013) · Foundations of Computational Mathematics · doi:10.1007/s10208-013-9150-3 · Cited by 654 · see citing works
On compressive sensing applied to radar
Ender, J. (2009) · Signal Processing · doi:10.1016/j.sigpro.2009.11.009 · Cited by 651 · see citing works
Introduction to Machine Learning
Suykens, J. A. K. (2014) · Academic press library in signal processing · doi:10.1016/b978-0-12-396502-8.00013-9 · Cited by 649 · see citing works
DiffuserCam: lensless single-exposure 3D imaging
Antipa, N., Kuo, G., Heckel, R., Mildenhall, B., Bostan, E., Ng, R., & Waller, L. (2017) · Optica · doi:10.1364/optica.5.000001 · Cited by 637 · see citing works
Closed-Loop Turbulence Control: Progress and Challenges
Brunton, S. L., & Noack, B. R. (2015) · Applied Mechanics Reviews · doi:10.1115/1.4031175 · Cited by 610 · see citing works
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Chen, P., Sharma, Y., Zhang, H., Yi, J., & Hsieh, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11302 · Cited by 594 · see citing works
Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing
Feng, C., Au, W. S. A., Valaee, S., & Tan, Z. (2011) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2011.216 · Cited by 591 · see citing works
Application of compressive sensing to sparse channel estimation
Berger, C. R., Wang, Z., Huang, J., & Zhou, S. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5621984 · Cited by 591 · see citing works
Transient phenomena in ecology
Hastings, A., Abbott, K. C., Cuddington, K. M., Francis, T. B., Gellner, G., Lai, Y., Morozov, A. Y., Petrovskii, S., Scranton, K., & Zeeman, M. L. (2018) · Science · doi:10.1126/science.aat6412 · Cited by 588 · see citing works
Manohar, K., Brunton, B. W., Kutz, J. N., & Brunton, S. L. (2018) · IEEE Control Systems · doi:10.1109/mcs.2018.2810460 · Cited by 583 · see citing works
Compressive data gathering for large-scale wireless sensor networks
Luo, C., Wu, F., Sun, J., & Chen, C. W. (2009) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/1614320.1614337 · Cited by 583 · see citing works
Pan, X., Sidky, E. Y., & Vannier, M. W. (2009) · Inverse Problems · doi:10.1088/0266-5611/25/12/123009 · Cited by 573 · see citing works
Fearnhead, P., & Prangle, D. (2012) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2011.01010.x · Cited by 550 · see citing works
Duarte‐Carvajalino, J. M., & Sapiro, G. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2022459 · Cited by 572 · see citing works
SLEP: Sparse Learning with Efficient Projections
Liu, J., Ji, S., & Ye, J. (2011) · Cited by 571 · see citing works
Vibrations, quanta and biology
Huelga, S. F., & Plenio, M. B. (2013) · Contemporary Physics · doi:10.1080/00405000.2013.829687 · Cited by 568 · see citing works
Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things
Li, S., Xu, L. D., & Wang, X. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2189222 · Cited by 564 · see citing works
Sparse Iterative Closest Point
Bouaziz, S., Tagliasacchi, A., & Pauly, M. V. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12178 · Cited by 553 · see citing works
Accelerated NMR Spectroscopy by Using Compressed Sensing
Kazimierczuk, K., & Orekhov, V. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201100370 · Cited by 550 · see citing works
A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications
Rani, M., Dhok, S. B., & Deshmukh, R. B. (2018) · IEEE Access · doi:10.1109/access.2018.2793851 · Cited by 535 · see citing works
Andrade, X., Strubbe, D. A., Giovannini, U. D., Larsen, A. H., Oliveira, M. J. T., Alberdi‐Rodriguez, J., Varas, A., Theophilou, I., Helbig, N., Verstraete, M. J., et al. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp00351b · Cited by 512 · see citing works
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing
He, L., & Carin, L. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2022003 · Cited by 507 · see citing works
Unconventional Phased Array Architectures and Design Methodologies—A Review
Rocca, P., Oliveri, G., Mailloux, R. J., & Massa, A. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2512389 · Cited by 502 · see citing works
Imaging with a small number of photons
Morris, P. A., Aspden, R. S., Bell, J., Boyd, R. W., & Padgett, M. J. (2015) · Nature Communications · doi:10.1038/ncomms6913 · Cited by 491 · see citing works
Dynamics of information diffusion and its applications on complex networks
Zhang, Z., Liu, C., Zhan, X., Lu, X. B., Zhang, C., & Zhang, Y. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.07.002 · Cited by 487 · see citing works
Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance
Blumensath, T., & Davies, M. E. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2042411 · Cited by 486 · see citing works
The Hiphive Package for the Extraction of High‐Order Force Constants by Machine Learning
Eriksson, F., Fransson, E., & Erhart, P. (2019) · Advanced Theory and Simulations · doi:10.1002/adts.201800184 · Cited by 479 · see citing works
Accelerating SENSE using compressed sensing
Liang, D., Liu, B., Wang, J., & Ying, L. L. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22161 · Cited by 468 · see citing works
Toward real-time terahertz imaging
Guerboukha, H., Nallappan, K., & Skorobogatiy, M. A. (2018) · Advances in Optics and Photonics · doi:10.1364/aop.10.000843 · Cited by 469 · see citing works
Group sparse regularization for deep neural networks
Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.029 · Cited by 455 · see citing works
Compressive sensing: From theory to applications, a survey
Qaisar, S., Bilal, R. M., Iqbal, W., Naureen, M., & Lee, S. (2013) · Journal of Communications and Networks · doi:10.1109/jcn.2013.000083 · Cited by 462 · see citing works
Comprehensive survey of image steganography: Techniques, Evaluations, and trends in future research
Kadhim, I. J., Premaratne, P., Vial, P. J., & Halloran, B. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.075 · Cited by 458 · see citing works
Statistical physics of inference: thresholds and algorithms
Zdeborová, L., & Krząkała, F. (2016) · Advances In Physics · doi:10.1080/00018732.2016.1211393 · Cited by 452 · see citing works
Single-pixel infrared and visible microscope
Radwell, N., Mitchell, K. J., Gibson, G. M., Edgar, M., Bowman, R. W., & Padgett, M. J. (2014) · Optica · doi:10.1364/optica.1.000285 · Cited by 449 · see citing works
Photonics for microwave measurements
Zou, X., Lü, B., Pan, W., Yan, L., Stöhr, A., & Yao, J. (2016) · Laser & Photonics Review · doi:10.1002/lpor.201600019 · Cited by 448 · see citing works
Block Compressed Sensing of Images Using Directional Transforms
Mun, S., & Fowler, J. E. (2010) · DCC · doi:10.1109/dcc.2010.90 · Cited by 437 · see citing works
Event-Triggered State Observers for Sparse Sensor Noise/Attacks
Shoukry, Y., & Tabuada, P. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2015.2492159 · Cited by 433 · see citing works
Shapiro, J. H., & Boyd, R. W. (2012) · Quantum Information Processing · doi:10.1007/s11128-011-0356-5 · Cited by 431 · see citing works
Nature of metal-support interaction for metal catalysts on oxide supports
Wang, T., Hu, J., Ouyang, R., Wang, Y., Huang, Y., Hu, S., & Li, W. (2024) · Science · doi:10.1126/science.adp6034 · Cited by 432 · see citing works
A comprehensive survey of anomaly detection techniques for high dimensional big data
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00320-x · Cited by 454 · see citing works
Nikos Komodakis, A., & Pesquet, J.-C. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2377273 · Cited by 420 · see citing works
Interconnecting Smart Objects with IP: The Next Internet
Vasseur, J.-P., & Dunkels, A. (2010) · Cited by 400 · see citing works
Compressive light field photography using overcomplete dictionaries and optimized projections
Marwah, K., Wetzstein, G., Bando, Y., & Raskar, R. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461914 · Cited by 400 · see citing works
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
You, C., Robinson, D. P., & Vidal, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.425 · Cited by 398 · see citing works
Zhou, F., Nielson, W., Xia, Y., & Ozoliņš, V. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.185501 · Cited by 385 · see citing works
A Comprehensive Review on Image Encryption Techniques
Kaur, M., & Kumar, V. (2018) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-018-9298-8 · Cited by 392 · see citing works
Chen, F., Chandrakasan, A. P., & Stojanovic, V. M. (2012) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2011.2179451 · Cited by 377 · see citing works
Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT
Bian, J., Siewerdsen, J. H., Han, X., Sidky, E. Y., Prince, J. L., Pelizzari, C. A., & Pan, X. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/22/001 · Cited by 375 · see citing works
Ghost imaging lidar via sparsity constraints
Zhao, C., Gong, W., Chen, M., Li, E., Wang, H., Xu, W., & Han, S. (2012) · Applied Physics Letters · doi:10.1063/1.4757874 · Cited by 369 · see citing works
D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization
Mota, J. F. C., Xavier, J., Aguiar, P. M. Q., & Püschel, M. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2254478 · Cited by 366 · see citing works
A review of acoustic imaging methods using phased microphone arrays
Merino-Martínez, R., Sijtsma, P., Snellen, M., Ahlefeldt, T., Antoni, J., Bahr, C. J., Blacodon, D., Ernst, D., Finez, A., Funke, S., et al. (2019) · CEAS Aeronautical Journal · doi:10.1007/s13272-019-00383-4 · Cited by 364 · see citing works
Compressive Sensing in Electromagnetics - A Review
Massa, A., Rocca, P., & Oliveri, G. (2015) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2015.2397092 · Cited by 363 · see citing works
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling
Dong, W., Lei Zhang, A., Rastislav Lukac, A., & Shi, G. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2231086 · Cited by 360 · see citing works
Compressed data aggregation for energy efficient wireless sensor networks
Liu, X., Luo, J., & Vasilakos, A. V. (2011) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2011.5984932 · Cited by 361 · see citing works
Sidky, E. Y., Jørgensen, J. S., & Pan, X. (2012) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/10/3065 · Cited by 360 · see citing works
Compressed Sensing for Wireless Communications: Useful Tips and Tricks
Choi, J. W., Shim, B. H., Ding, Y., Rao, B. D., & Kim, D. I. (2017) · The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry) · doi:10.1109/comst.2017.2664421 · Cited by 355 · see citing works
Predicting Catastrophes in Nonlinear Dynamical Systems by Compressive Sensing
Wang, W.-X., Yang, R., Lai, Y., Kovanis, V. I., & Grebogi, C. (2011) · Physical Review Letters · doi:10.1103/physrevlett.106.154101 · Cited by 354 · see citing works
Handbook of Mathematical Methods in Imaging
Lassas, M. J., Salo, M., & Uhlmann, G. A. (2010) · Springer eBooks · doi:10.1007/978-0-387-92920-0 · Cited by 352 · see citing works
Computational illumination for high-speed in vitro Fourier ptychographic microscopy
Tian, L., Liu, Z., Yeh, L.-H., Chen, M., Zhong, J., & Waller, L. (2015) · Optica · doi:10.1364/optica.2.000904 · Cited by 350 · see citing works
The secrecy of compressed sensing measurements
Rachlin, Y., & Baron, D. (2008) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2008.4797641 · Cited by 350 · see citing works
Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
Lu, C., Tang, J., Yan, S., & Lin, Z. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2511584 · Cited by 349 · see citing works
Sparse Modeling for Image and Vision Processing
Mairal, J. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781680830095 · Cited by 332 · see citing works
Liu, Y., Ma, J., Fan, Y., & Liang, Z. (2012) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/23/7923 · Cited by 335 · see citing works
AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing
Zhang, Z., Liu, Y., Liu, J., Wen, F., & Zhu, C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3044472 · Cited by 334 · see citing works
Atomic-scale determination of surface facets in gold nanorods
Goris, B., Bals, S., den Broek, W. V., Carbó‐Argibay, E., Gómez‐Graña, S., Liz‐Marzán, L. M., & TENDELOO, G. V. (2012) · Nature Materials · doi:10.1038/nmat3462 · Cited by 334 · see citing works
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges
Khalajmehrabadi, A., Gatsis, N., & Akopian, D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2671454 · Cited by 339 · see citing works
Simultaneous real-time visible and infrared video with single-pixel detectors
Edgar, M., Gibson, G. M., Bowman, R. W., Sun, B., Radwell, N., Mitchell, K. J., Welsh, S. S., & Padgett, M. J. (2015) · Scientific Reports · doi:10.1038/srep10669 · Cited by 333 · see citing works
Kernel Sparse Representation-Based Classifier
Zhang, L., Zhou, W., Chang, P., Liu, J., Yan, Z., Wang, T., & Li, F. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2179539 · Cited by 332 · see citing works
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 317 · see citing works
Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition
Gemmeke, J. F., Virtanen, T. I., & Hurmalainen, A. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2112350 · Cited by 328 · see citing works
- Authors
- Candes, E. J., & Wakin, M. B.
- Affiliations
- École Polytechnique (FR)
- ORCID
- Wakin, M.B.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2008-03
- Volume
- 25
- Issue
- 2
- Pages
- 21-30
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2119667497
- Cited by
- 10,017
- References
- 32
- Citations by year
- 2026: 233 · 2025: 356 · 2024: 359 · 2023: 437 · 2022: 449 · 2021: 561 · 2020: 545 · 2019: 727 · 2018: 818 · 2017: 792 · 2016: 823 · 2015: 899 · 2014: 803 · 2013: 709 · 2012: 614
- FWCI
- 310.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
- Keywords
- compressed sensing, Nyquist rate, undersampling, signal reconstruction, image reconstruction, analog-to-digital conversion, data acquisition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
