• Emmanuel Candès, Compressive Sampling. ((Int. Congress of Mathematics, 3, pp. 1433-1452, Madrid, Spain, 2006))
• Richard Baraniuk, Compressive sensing.
  (IEEE Signal Processing Magazine, 24(4), pp. 118-121, July 2007)
• Emmanuel Candès and Michael Wakin,
  An introduction to compressive sampling.
  (IEEE Signal Processing Magazine, 25(2), pp. 21 - 30, March 2008) [High-resolution version]
• Justin Romberg,
  
  Imaging via compressive sampling.
  (IEEE Signal Processing Magazine, 25(2), pp. 14 - 20, March 2008)
• Dana Mackenzie, Compressed Sensing Makes Every Pixel Count. (Mackenzie, Dana (2009), "Compressed sensing makes every pixel count", What's Happening in the Math. Sciences, AMS, 114-127)
• Richard Baraniuk, More Is less: Signal processing and the data deluge. (Science 331 (6018), pp. 717 - 719, February 2011)
• Massimo Fornasier and Holger Rauhut, Compressive sensing. (Chapter in Part 2 of the Handbook of Mathematical Methods in Imaging (O. Scherzer Ed.), Springer, 2011)
• Mark Davenport, Marco Duarte, Yonina Eldar, and Gitta Kutyniok,
  
  Introduction to compressed sensing,
  (Chapter in Compressed Sensing: Theory and Applications, Cambridge University Press, 2012)
• Marco Duarte and Yonina Eldar, Structured compressed sensing: Theory and applications. (To appear in IEEE Transactions on Signal Processing)
• Rebecca Willett, Roummel Marcia, and Jonathan Nichols, Compressed sensing for practical optical imaging systems: a tutorial. (Optical Engineering, vol. 50, no. 7, pp. 072601 1-13, 2011)
• L. Jacques and P. Vandergheynst, "Compressed Sensing: When sparsity meets sampling". ((see below, this box is too small))
• Gitta Kutyniok, Theory and Applications of Compressed Sensing. (Preprint)
• Sergios Theodoridis, Yannis Kopsinis, Konstantinos Slavakis, Sparsity-Aware Learning and Compressed Sensing: An Overview. (arXiv:1211.5231)
• Jordan Ellenberg, Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples. (Wired Magazine, March 2010)
• Saad Qaisar, Rana Muhammad Bilal, Wafa Iqbal, Muqaddas Naureen and Sungyoung Lee, Compressive Sensing: From Theory to Applications, A Survey. (Saad Qaisar et al. , To Appear in Journal of Communication and Networks, July, 2013)
• Kazunori Hayashi, Masaaki Nagahara, and Toshiyuki Tanaka, A User's Guide to Compressed Sensing for Communications Systems. (IEICE Trans. on Communications, Vol. E96-B, No. 3, pp. 685-712, Mar. 2013) [SCILAB Codes]
• Hong Huang, Satyajayant Misra, Wei Tang, Hajar Barani, and Hussein Al-Azzawi, Applications of Compressed Sensing in Communications Networks. (arXiv:1305.3002v3)
• See below for tutorial talks on compressive sensing.

• Emmanuel Candès, Justin Romberg, and Terence Tao,
  
  Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information.
  (IEEE Trans. on Information Theory, 52(2) pp. 489 - 509, February 2006)
• Emmanuel Candès and Justin Romberg,
  
  Quantitative robust uncertainty principles and optimally sparse decompositions.
  (Foundations of Comput. Math., 6(2), pp. 227 - 254, April 2006)
• David Donoho,
  
  Compressed sensing.
  (IEEE Trans. on Information Theory, 52(4), pp. 1289 - 1306, April 2006)
• Emmanuel Candès and Terence Tao,
  
  Near optimal signal recovery from random projections: Universal encoding strategies?
  (IEEE Trans. on Information Theory, 52(12), pp. 5406 - 5425, December 2006)
• Emmanuel Candès and Justin Romberg,
  
  Practical signal recovery from random projections.
  (Preprint, Jan. 2005)
• David Donoho and Yaakov Tsaig,
  
  Extensions of compressed sensing.
  (Signal Processing, 86(3), pp. 533-548, March 2006)
• Emmanuel Candès, Justin Romberg, and Terence Tao,
  
  Stable signal recovery from incomplete and inaccurate measurements.
  (Communications on Pure and Applied Mathematics, 59(8), pp. 1207-1223, August 2006)
• Jarvis Haupt and Rob Nowak,
  
  Signal reconstruction from noisy random projections.
  (IEEE Trans. on Information Theory, 52(9), pp. 4036-4048, September 2006)
• Emmanuel Candès and Terence Tao,
  
  The Dantzig Selector: Statistical estimation when p is much larger than n
  (To appear in Annals of Statistics)
• Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin,
  
  A simple proof of the restricted isometry property for random matrices.
  (Constructive Approximation, 28(3), pp. 253-263, December 2008) [Formerly titled "The Johnson-Lindenstrauss lemma meets compressed sensing"]
• Albert Cohen, Wolfgang Dahmen, and Ronald DeVore,
  
  Compressed sensing and best k-term approximation.
  (Preprint, 2006) [Formerly titled "Remarks on compressed sensing"]
• Martin J. Wainwright,
  
  Sharp thresholds for high-dimensional and noisy recovery of sparsity
  (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2006)
• Holger Rauhut, Karin Schnass, and Pierre Vandergheynst,
  
  Compressed sensing and redundant dictionaries.
  (IEEE Trans. on Information Theory, 54(5), pp. 2210 - 2219, May 2008)
• Emmanuel Candès and Justin Romberg,
  
  Sparsity and incoherence in compressive sampling.
  (Inverse Problems, 23(3) pp. 969-985, 2007)
• Ronald A. DeVore,
  
  Deterministic constructions of compressed sensing matrices.
  (J. of Complexity, 23, pp. 918 - 925, August 2007)
• Piotr Indyk,
  
  Explicit constructions for compressed sensing of sparse signals.
  (Symp. on Discrete Algorithms, 2008)
• Yin Zhang,
  
  A simple proof for recoverability of ell-1-minimization: go over or under?
  (Rice CAAM Department Technical Report TR05-09, 2005)
• Yin Zhang,
  
  A simple proof for recoverability of ell-1-minimization (II): the nonnegative case.
  (Rice CAAM Department Technical Report TR05-10, 2005)
• Yin Zhang,
  
  When is missing data recoverable?
  (Rice CAAM Department Technical Report TR05-15, 2005)
• Boris S. Kashin and Vladimir N. Temlyakov,
  
  A remark on compressed sensing.
  (Matem. Zametki, 82, pp. 821--830, 2007)
• Waheed Bajwa, Jarvis Haupt, Gil Raz, Stephen Wright, and Robert Nowak,
  
  Toeplitz-structured compressed sensing matrices.
  (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007)
• Weiyu Xu and Babak Hassibi,
  
  Efficient compressive sensing with determinstic guarantees using expander graphs.
  (IEEE Information Theory Workshop, Lake Tahoe, September 2007)
• Yoav Sharon, John Wright, and Yi Ma,
  
  Computation and relaxation of conditions for equivalence between ell-1 and ell-0 minimization.
  (Preprint, 2007)
• Thong T. Do, Trac D. Tran, and Lu Gan,
  
  Fast compressive sampling with structurally random matrices.
  (Preprint, 2007)
• Radu Berinde and Piotr Indyk,
  
  Sparse recovery using sparse random matrices.
  (Preprint, 2008)
• P. Wojtaszczyk,
  
  Stability and instance optimality for Gaussian measurements in compressed sensing.
  (Preprint, 2008)
• Venkat Chandar,
  
  A negative result concerning explicit matrices with the restricted isometry property.
  (Preprint, 2008)
• Florian Sebert, Leslie Ying, and Yi Ming Zou,
  
  Toeplitz block matrices in compressed sensing.
  (Preprint, 2008)
• Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal,
  
  Necessary and sufficient conditions on sparsity pattern recovery.
  (Submitted to IEEE Trans. Information Theory)
• R. Berinde, A. C. Gilbert, P. Indyk, H. Karloff, and M. J. Strauss,
  
  Combining geometry and combinatorics: A unified approach to sparse signal recovery.
  (Preprint, 2008)
• Dapo Omidiran and Martin J. Wainwright,
  
  High-dimensional subset recovery in noise: Sparsified measurements without loss of statistical efficiency.
  (Preprint, 2008)
• Sina Jafarpour, Weiyu Xu, Babak Hassibi, and Robert Calderbank,
  
  Efficient compressed sensing using high-quality expander graphs.
  (to appear in IEEE Trans Info Theory, 2009)
• Shamgar Gurevich, Ronny Hadani, and Nir Sochen,
  
  On some deterministic dictionaries supporting sparsity.
  (To appear in Journal of Fourier Analysis and Applications)
• Emmanuel Candès,
  
  The restricted isometry property and its implications for compressed sensing.
  (Compte Rendus de l'Academie des Sciences, Paris, Series I, 346, pp. 589-592, 2008)
• T. Tony Cai, Guangwu Xu, and Jun Zhang,
  
  On recovery of sparse signals via ell-1 minimization.
  (Preprint, 2008)
• Venkatesh Saligrama,
  
  Deterministic designs with deterministic guarantees: Toeplitz compressed sensing matrices, sequence design and system identification.
  (Preprint, 2008)
• Weiyu Xu and Babak Hassibi,
  
  Compressed sensing over the Grassmann manifold: A unified analytical framework.
  (Preprint, 2008)
• Justin Romberg,
  
  Compressive sensing by random convolution.
  (Preprint, 2008)
• Yin Zhang,
  
  On theory of compressive sensing via ell-1-minimization: Simple derivations and extensions.
  (Rice CAAM Department Technical Report TR08-11, 2008)
• Ronald DeVore, Guergana Petrova, and Przemyslaw Wojtaszczyk,
  
  Instance-optimality in probability with an ell-1 decoder.
  (Preprint, 2008)
• Shamgar Gurevich and Ronny Hadani,
  
  Incoherent dictionaries and the statistical restricted isometry property.
  (Preprint, 2008)
• Jarvis Haupt, Waheed U. Bajwa, Gil Raz, and Robert Nowak,
  
  Toeplitz compressed sensing matrices with applications to sparse channel estimation.
  (Preprint, 2008)
• Anatoli Juditsky and Arkadi Nemirovskim,
  
  On verifiable sufficient conditions for sparse signal recovery via ell-1 minimization.
  (Preprint, 2008)
• J.L. Nelson and V.N. Temlyakov,
  
  On the size of incoherent systems.
  (Preprint, 2008)
• Yaron Rachlin and Dror Baron,
  
  The secrecy of compressed sensing measurements.
  (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2008)
• Robert Calderbank, Stepen Howard, and Sina Jafarpour,
  
  Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property.
  (To appear in the Compressed Sensing Special Issue of IEEE Journal of Selected Topics in Signal Processing)
• Jeffrey Blanchard, Coralia Cartis, and Jared Tanner,
  
  Compressed Sensing: How Sharp is the Restricted Isometry Property?. [Extended Tech Report] (Under revision, 2009)
• Jeffrey Blanchard, Coralia Cartis, and Jared Tanner,
  
  Decay properties of restricted isometry constants.
  (IEEE Signal Processing Letters, 16(7), 572-575, 2009)
• Mark Iwen,
  
  Simple Deterministically Constructible RIP Matrices with Sublinear Fourier Sampling Requirements .
  (Preprint, CISS 2009, Baltimore, MD)
• M.Amin Khajehnejad, Alexandros G. Dimakis, Weiyu Xu, Babak Hassibi,
  
  Sparse Recovery of Positive Signals with Minimal Expansion .
  (Preprint, 2009)
• Matthew A. Herman and Thomas Strohmer,
  
  General Deviants: An Analysis of Perturbations in Compressed Sensing .
  (Preprint, 2009)
• Holger Rauhut,
  
  Circulant and Toeplitz matrices in compressed sensing.
  (In Proc. SPARS'09, Saint Malo, 2009)
• Gilles Gnacadja,
  
  Counting the Scaled +1/-1 Matrices that Satisfy the Restricted Isometry Property.
  (Preprint, 2009)
• P. Wojtaszczyk,
  
  Stability of l1 minimization in compressed sensing.
  (in Proc. SPARS'09 St. Malo 2009)
• Lu Gan, Cong Ling, Thong T. Do, and Trac D. Tran,
  
  Analysis of the statistical restricted isometry property for deterministic sensing matrices using Stein’s method.
  (Preprint, 2009)
• Paul Tune, Sibi Raj Bhaskaran, Stephen Hanly,
  
  Number of measurements in sparse signal recovery.
  (ISIT 2009, to appear)
• Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner, Andrew Thompson,
  
  Phase transitions for greedy sparse approximation algorithms.
  (Preprint, August 2009)
• Jeffrey D. Blanchard, Andrew Thompson,
  
  On support sizes of restricted isometry constants.
  (Preprint, August 2009)
• Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal,
  
  Asymptotic analysis of MAP estimation via the replica method and applications to compressed sensing.
  (Submitted to IEEE Trans. Information Theory)
• Tomas Tuma, Paul Hurley,
  On the incoherence of noiselet and Haar bases.
  (SAMPTA 2009)
• Jason Laska, Mark Davenport, and Richard Baraniuk,
  
  Exact signal recovery from sparsely corrupted measurements through the pursuit of justice.
  (Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, California, November 2009)
• Mark Davenport, Jason Laska, Petros Boufounos, and Richard Baraniuk,
  
  A simple proof that random matrices are democratic.
  (Rice University ECE Department Technical Report TREE-0906, November 2009)
• T. Tony Cai, Le Wang, Guangwu Xu,
  
  New bounds for restricted isometry constants.
  (Preprint, Nov 2009)
• D. Guo, D. Baron, S. Shamai,
  
  A single-letter characterization of optimal noisy compressed sensing.
  (Asilomar Conf. on Signals, Systems, and Computers, Monterey, California, November 2009)
• G. Reeves, M. Gastpar,
  
  A note on optimal support recovery in compressed sensing.
  (Asilomar Conf. on Signals, Systems, and Computers, Monterey, CA, November 2009)
• Vladimir N. Temlyakov and Pavel Zheltov,
  
  On performance of greedy algorithms.
  (Submitted to Journal of Approximation Theory, Jan 2010)
• Gongguo Tang, Arye Nehorai,
  
  Performance analysis for sparse support recovery.
  (Preprint, Nov 2009)
• Bubacarr Bah, Jared Tanner,
  
  Improved bounds on restricted isometry constants for Gaussian matrices.
  (Preprint, March 2010)
• Robert Calderbank and Sina Jafarpour,
  
  Reed Muller Sensing Matrices and the Lasso
  (Preprint, April 2010)
• Waheed U. Bajwa, Robert Calderbank, and Sina Jafarpour,
  
  Model selection: Two fundamental measures of coherence and their algorithmic significance
  (Proc. IEEE Int. Symp. Information Theory, June 2010)
• Waheed U. Bajwa, Robert Calderbank, and Sina Jafarpour, Why Gabor Frames? Two Fundamental Measures of Coherence and Their Role in Model Selection. (Submitted for Publication)
• Charles Dossal, Gabriel Peyré, Jalal Fadili, A Numerical Exploration of Compressed Sampling Recovery. (Linear Algebra and its Applications, Vol. 432(7), p.1663-1679, 2010)
• Maxim Raginsky, Rebecca Willett, Zachary Harmany, and Roummel Marcia., Compressed sensing performance bounds under Poisson noise. (IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 3990-4002, 2010)
• Maxim Raginsky, Sina Jafarpour, Zachary Harmany, Roummel Marcia, Rebecca Willett, and Robert Calderbank, Performance bounds for expander-based compressed sensing in Poisson noise. (Submitted to IEEE Transactions on Signal Processing, 2010)
• Jae Young Park, Han Lun Yap, Christopher J. Rozell, Michael B. Wakin, Concentration of Measure for Block Diagonal Matrices with Applications to Compressive Sensing. (preprint)
• Zhiqiang Xu, Deterministic Sampling of Sparse Trigonometric Polynomials. (arXiv:1006.2221)
• Kezhi Li, Lu Gan, and Cong Ling, Orthogonal Symmetric Toeplitz Matrices for Compressed Sensing: Statistical Isometry Property. (submitted for publication, Dec. 2010.)
• Laurent Gosse, Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions. (Preprint (2010))
• Arian Maleki, Laura Anitori, Zai Yang, and Richard Baraniuk, Asymptotic Analysis of Complex LASSO via Complex Approximate Message Passing (CAMP) . (submitted to IEEE Trans. on Information Theory)
• Dornoosh Zonoobi, Ashraf A. Kassim, Yedatore V. Venkatesh, Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples. (IEEE journal of selected topics in signal processing)
• Justin Ziniel, Lee C. Potter, and Philip Schniter, Tracking and Smoothing of Time-Varying Sparse Signals via Approximate Belief Propagation. (Asilomar Conf. on Signals, Systems, and Computers (SS&C;), (Pacific Grove, CA), Nov. 2010)
• Kanke Gao, Stella N. Batalama, Dimitris A. Pados, and Bruce W. Suter, Compressive Sampling with Generalized Polygons. (IEEE Trans. on Signal Processing, to appear October 2011)
• Emmanuel Candès and Mark Davenport,
  How well can we estimate a sparse vector?
  (Preprint, April 2011)
• Gongguo Tang and Arye Nehorai, Verifiable and computable performance analysis of sparsity recovery. (submitted for publication, arXiv:1102.4868)
• Gongguo Tang and Arye Nehorai, Fixed point theory and semidefinite programming for computable performance analysis of block-sparsity recovery. (Submitted for publication, arXiv: 1110.1078)
• Amin Khajehnejad, Weiyu Xu, Alex Dimakis and Babak Hassibi, Sparse Recovery of Nonnegative Signals with Minimal Expansion. (IEEE Transactions on Signal Processing, 2010, Vol. 59 (1), pp. 196-208)
• Jian Wang and Byonghyo Shim, On the Recovery Limit of Sparse Signals using Orthogonal Matching Pursuit. (To appear in IEEE Trans. on Signal Process. )
• Yoav Sharon, John Wright, and Yi Ma, Minimum sum of distances estimator: Robustness and stability. (Proc. 2009 American Control Conference (ACC '09), pp. 524-530, June 2009)
• Andreas M. Tillmann and Marc E. Pfetsch, The Computational Complexity of RIP, NSP, and Related Concepts in Compressed Sensing. (arXiv:1205.2081)
• Massoud Babaie-Zadeh, Christian Jutten, and Hossein Mohimani, On the error of estimating the sparsest solution of underdetermined linear systems. (EEE Transaction on Information Theory, vol. 57, no. 12, pp. 7840-7855, December 2011)
• T. Wimalajeewa, H. Chen and P. K. Varshney, Performance Limits of Compressive Sensing-Based Signal Classification. (IEEE Trans. on Signal Processing, vol.60, no.6, pages 2758-2770, June 2012)
• Weizhi Lu, Kidiyo Kpalma and Joseph Ronsin, Sparse Binary Matrices of LDPC codes for Compressed Sensing. (Data Compression Conference (DCC), April 10 -12, 2012, Snowbird, Utah, USA.)
• Mahdi S. Hosseini, Siavash Fazeli-Dehkordy and Konstantinos N. Plataniotis, Tractable Bound for Spherical Section Property in the Presence of Side-Information. (IEEE Signal Processing Letters, PP(99), June 2012)
• J. Wang, S. Kwon, and B. Shim, Generalized orthogonal matching pursuit. (to appear in IEEE Trans. Signal Process. Dec. 2012)
• Gongguo Tang, Badri Narayan Bhaskar, Parikshit Shah, Benjamin Recht, Compressed Sensing off the Grid. (arXiv preprint: 1207.6053)
• J. Wang, S. Kwon, and B. Shim, Generalized orthogonal matching pursuit. (IEEE Trans. Signal Process. vol. PP, no. 99, pp.1, 0, doi: 10.1109/TSP.2012.2218810)
• Ukash Nakarmi, Nazanin Rahnavard, BCS: Compressive Sensing for Binary Sparse Signals. (MILCOM 2012)
• Mahdi Cheraghchi, Venkatesan Guruswami, Ameya Velingker, Restricted Isometry of Fourier Matrices and List Decodability of Random Linear Codes. (In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2013.)
• Kezhi Li, Lu Gan, Cong Ling, Convolutional Compressed Sensing Using Deterministic Sequences. (IEEE Trans. on Signal Processing, to appear)
• Borhan M. Sanandaji, Tyrone L. Vincent, Michael B. Wakin, Concentration of Measure Inequalities for Toeplitz Matrices with Applications. (IEEE Trans. on Signal Processing, 61(1), pp. 109-117, January 2013) [http://arxiv.org/abs/1112.1968]
• Yuxin Chen and Yuejie Chi, Spectral Compressed Sensing via Structured Matrix Completion. (accepted to International Conference on Machine Learning (ICML 2013))
• Giulio Coluccia, Aline Roumy and Enrico Magli, Exact Performance Analysis of the Oracle Receiver for Compressed Sensing Reconstruction. (39th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Firenze, Italy, May 4-9, 2014)
• Weiyu Xu, Jian-Feng Cai, Kumar Vijay Mishra, Myung Cho, and Anton Kruger, Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional (d >= 2) Off-The-Grid Freq. (IEEE Information Theory and Applications Workshop (ITA), Feb 9-14 2014, San Diego, California)
• Kumar Vijay Mishra, Myung Cho, Anton Kruger, Weiyu Xu, Off-The-Grid Spectral Compressed Sensing With Prior Information. (39th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 4-9, 2014)
• Zai Yang and Lihua Xie, Exact Joint Sparse Frequency Recovery via Optimization Methods. (Preprint, 2014)
• Kumar Vijay Mishra, Myung Cho, Anton Kruger, Weiyu Xu, Off-The-Grid Spectral Compressed Sensing With Prior Information. ( IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 4-9 2014, Florence, Italy)
• Kumar Vijay Mishra, Myung Cho, Anton Kruger, Weiyu Xu, Super-resolution Line Spectrum Estimation with Block Priors. (48th Asilomar Conference on Signals, Systems and Computers, Nov 2-5 2014, Asilomar Conference Grounds, Pacific Grove, California)
• Kumar Vijay Mishra, Myung Cho, Anton Kruger, and Weiyu Xu, Spectral super-resolution with prior knowledge. (arXiv:1409.1673v1 [cs.IT] )
• M. Eren Ahsen and M. Vidyasagar, Error bounds for compressed sensing algorithms with group sparsity: A unified approach. (Applied and Computational Harmonic Analysis)

• Gabriel Peyré,
  
  Best basis compressed sensing.
  (IEEE Transactions on Signal Processing, Vol. 58(5), p.2613-2622 , 2010) [See also related conference publication:
  NeuroComp 2006]
• Yue Lu and Minh Do,
  
  A theory for sampling signals from a union of subspaces.
  (IEEE Trans. on Signal Processing, 56(6), pp. 2334 - 2345, June 2008)
• Lawrence Carin, Dehong Liu, and Ya Xue,
  
  In Situ Compressive Sensing.
  (Inverse Problems, 24(1), Feb. 2008)
  [See also related conference publication: SSP 2007]
• Remi Gribonval and Morten Nielsen,
  
  Beyond sparsity : recovering structured representations by ell-1-minimization and greedy algorithms - Application to the analysis of sparse underdetermined ICA.
  (To appear in Advances in Computational Mathematics)
• Cynthia Dwork, Frank McSherry, and Kunal Talwar,
  
  The price of privacy and the limits of LP decoding.
  (Symp. on Theory of Computing (STOC), San Diego, California, June, 2007)
• Akram Aldroubi, Carlos Cabrelli, and Ursula Molter,
  
  Optimal non-linear models for sparsity and sampling.
  (Preprint, 2007)
• Lawrence Carin, Dehong Liu, Wenbin Lin, and Bin Guo,
  
  Compressive sensing for multi-static scattering analysis.
  (Preprint, 2007)
• Benjamin Rect, Maryam Fazel, and Pablo A. Parrilo,
  
  Guaranteed minimum-rank solution of linear matrix equations via nuclear norm minimization.
  (Preprint, 2007)
• Mona Sheikh and Richard Baraniuk,
  
  Blind error-free detection of transform-domain watermarks.
  (IEEE Int. Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007)
• Gotz Pfander, Holger Rauhut, and Jared Tanner,
  
  Identification of matrices having a sparse representation.
  (Preprint, 2007) [See also related note]
• Gotz Pfander and Holger Rauhut,
  
  Sparsity in time-frequency representations.
  (Preprint, 2007)
• Alfred M. Bruckstein, Michael Elad, and Michael Zibulevsky,
  
  A non-negative and sparse enough solution of an underdetermined linear system of equations is unique.
  (Preprint, 2007)
• Thomas Blumensath and Mike E. Davies,
  
  Sampling theorems for signals from the union of linear subspaces.
  (Preprint, 2007)
• Rick Chartrand and Valentina Staneva,
  
  Restricted isometry properties and nonconvex compressive sensing.
  (Inverse Problems, vol. 24, no. 035020, pp. 1--14, 2008)
• Emmanuel Candès and Yaniv Plan,
  
  Near-ideal model selection by ell-1 minimization.
  (Preprint, 2007)
• Basarab Matei and Yves Meyer,
  
  A variant on the compressed sensing of Emmanuel Candès.
  (Preprint, 2008)
• Rachel Ward,
  
  Compressed sensing with cross validation.
  (Preprint, 2008) [Formerly titled "Cross validation in compressed sensing via the Johnson Lindenstrauss Lemma"]
• Vivek K Goyal, Alyson K. Fletcher, and Sundeep Rangan,
  
  Compressive sampling and lossy compression.
  (IEEE Signal Processing Magazine, 25(2), pp. 48-56, March 2008)
• Petros Boufounos and Richard G. Baraniuk,
  
  Reconstructing sparse signals from their zero crossings.
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Namrata Vaswani,
  
  Kalman filtered compressed sensing.
  (IEEE Int. Conf. on Image Processing (ICIP), San Diego, California, October 2008)
• Lawrence Carin, Dehong Liu, and Bin Guo,
  
  In situ compressive sensing for multi-static scattering: Imaging and the restricted isometry property.
  (Preprint, 2008)
• Emmanuel Candès and Benjamin Recht,
  
  Exact matrix completion via convex optimization.
  (Preprint, 2008)
• Rayan Saab, Rick Chartrand, and Özgür Yilmaz,
  
  Stable sparse approximation via nonconvex optimization.
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Mike E. Davies and Rémi Gribonval,
  
  Restricted isometry constants where ell-p sparse recovery can fail for 0 < p <= 1.
  (Preprint, 2008)
• Simon Foucart and Ming-Jun Lai,
  
  Sparsest solutions of underdetermined linear systems via ell-q minimization for 0 < q <= 1.
  (Preprint, 2008)
• Rayan Saab and Özgür Yilmaz,
  
  Sparse recovery by non-convex optimization - instance optimality.
  (Preprint, 2008)
• Yonina Eldar,
  
  Uncertainty relations for analog signals.
  (Preprint, 2008)
• Giuseppe Valenzise, Giorgio Prandi, Mario Tagliasacchi, and Augusto Sarti,
  
  Identification of sparse audio tampering using distributed source coding and compressive sensing techniques.
  (Preprint, 2008) [See also related conference publication:
  DAFX 2008]
• Marco Tagliasacchi, Giuseppe Valenzise, and Stefano Tubaro,
  Hash-based identification of sparse image tampering. (Preprint,
  2008) [See also related conference publication:
  ICIP 2008]
• Jian-Feng Cai, Emmanuel Candès , and Zuowei Shen,
  
  A singular value thresholding algorithm for matrix completion.
  (Preprint, 2008)
• Dmitry Malioutov, Sujay Sanghavi, and Alan Willsky,
  
  Compressed sensing with sequential observations.
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Justin P. Haldar, Diego Hernando,
  
  Rank-Constrained Solutions to Linear Matrix Equations using PowerFactorization.
  (IEEE Signal Processing Letters, 16:584-587, 2009)
• Massimo Fornasier and Rachel Ward,
  
  Iterative thresholding meets free discontinuity problems.
  (Preprint, January 2009)
• Magali Anastasio and Carlos Cabrelli,
  
  Sampling in a union of frame generated subspaces.
  (Preprint, 2009)
• D. Angelosante, E. Grossi, G. B. Giannakis,
  
  Compressed Sensing of time-varying signals.
  (DSP 2009, Santorini, Greece)
• Joshua Trzasko, Armando Manduca,
  
  Relaxed Conditions for Sparse Signal Recovery with General Concave Priors.
  (to appear in IEEE Trans. Signal Processing, 2009)
• G. Gasso, A. Rakotomamonjy, S. Canu,
  
  Recovering sparse signals with non-convex penalties and DC programming.
  (IEEE Trans. Signal Processing, to appear, 2009)
• Thomas Blumensath,
  
  Sampling and reconstructing signals from a union of linear subspaces.
  (Preprint, November 2009)
• Petros T. Boufounos, Gitta Kutyniok and Holger Rauhut,
  
  Sparse Recovery from Combined Fusion Frame Measurements.
  (Accepted to IEEE Trans Information Theory)
• Namrata Vaswani,
  
  LS-CS-residual (LS-CS): Compressive Sensing on the Least Squares Residual.
  (Accepted to IEEE Trans. Signal Processing)
• Namrata Vaswani, Wei Lu,
  
  Modified-CS: Modifying Compressive Sensing for Problems with Partially Known Support.
  (Revised and resubmitted to IEEE Trans. Signal Processing)
• Laurent Jacques, A Short Note on Compressed Sensing with Partially Known Signal Support. (Signal Processing, (in press), doi:10.1016/j.sigpro.2010.05.025) [http://arxiv.org/abs/0908.0660]
• Sadegh Jokar, Volker Mehrmann, Marc Pfetsch, Harry Yserentant , Sparse Approximate Solution of Partial Differential Equations. (Applied Numerical Mathematics 60, No. 4 (2010), 452-472)
• Sadegh Jokara, Volker Mehrmann, Sparse solutions to underdetermined Kronecker product systems. (Linear Algebra and its Applications, 431(12), pp. 2437-2447, December 2009)
• Volkan Cevher, Learning with Compressible Priors. (NIPS 2009)
• Qiyu Sun, Recovery of sparsest signals via $\ell^q$-minimization. (Preprint, 2010)
• Massimo Fornasier, Karin Schnass, and Jan Vybiral, Learning Functions of Few Arbitrary Linear Parameters in High Dimensions. (preprint, arXiv:1008.3043v1 [math.NA])
• Massimo Fornasier, Holger Rauhut, and Rachel Ward, Low-rank matrix recovery via iteratively re-weighted least squares minimization. (preprint, October 2010)
• Omid Taheri and Sergiy A. Vorobyov, Segmented compressed sampling for analog-to-information conversion: Method and performance analysis. (accepted for publication in IEEE Trans. Signal Processing )
• Irina Rish and Genady Grabarnik, Sparse Signal Recovery with Exponential-Family Noise. (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2009)
• Yangyang Xu, Wotao Yin, Zaiwen Wen, Yin Zhang, An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors. (Rice CAAM Technical Report TR11-03)
• Chenlu Qiu, Namrata Vaswani, Real-tine Robust Principal Components' Pursuit. (Allerton Conference on Communication, Control, and Computing, Monticello, IL, Oct 2010)
• David L. Donoho and Gitta Kutyniok, . Microlocal Analysis of the Geometric Separation Problem (Preprint, 2010)
• Yue Hu, Sajan Goud Lingala, Mathews Jacob, A fast majorize-minimize algorithm for the recovery of sparse and low rank matrices. (IEEE Trans. on Image Processing)
• Gongguo Tang and Arye Nehorai, Constrained Cramérâ��Rao bound for robust principal component analysis. (IEEE Trans. Signal Processing, vol. 59, no. 10, pp. 5070-5076, Oct. 2011.)
• Gongguo Tang and Arye Nehorai, Lower bounds on mean-squared error for low-rank matrix reconstruction. (IEEE Trans. Signal Processing, vol. 59, no. 10, pp. 4559-4571, Oct. 2011.)
• Yue Hu and Mathews Jacob, Higher Degree total variation (HDTV) regularization for image recovery. ( IEEE Trans. on Image Processing, accepted)
• Li-Wei Kang, Chao-Yung Hsu, Hung-Wei Chen, Chun-Shien Lu, Chih-Yang Lin, and Soo-Chang Pei, Feature-based Sparse Representation for Image Similarity Assessmen. (IEEE Trans. on Multimedia, Vol. 13, No. 5, pp. 1019-1030, 2011.)
• Li-Wei Kang and Chun-Shien Lu, Compressive Sensing-based Image Hashing. (Proc. IEEE Int. Conf. on Image Processing, pp. 1285-1289, November 7-11, 2009.)
• Yair Rivenson and Adrian Stern, Compressed imaging with separable sensing operator. (IEEE Signal Processing Letters, 16(6), 449-452 )
• Mahdi S. Hosseini and Oleg Michailovich, Derivative compressive sampling with application to phase unwrapping. (in Proc. of the 17th European Signal Processing Conference (EUSIPCO), August 24-28, Glasgow, UK, 2009.)
• Mahdi Cheraghchi, Improved Constructions for Non-adaptive Threshold Group Testing. (In Proceedings of the 37th International Colloquium on Automata, Languages and Programming (ICALP), 2010.)
• Mahdi Cheraghchi, Amin Karbasi, Soheil Mohajer, Vankatesh Saligrama, Graph-Constrained Group Testing. (IEEE Transactions on Information Theory 58(1), pp 248-262, 2012.)
• Mahdi S. Hosseini, Derivative Compressive Sampling and its Application to Inverse Problems and Imaging. (M.A.Sc. Thesis, ECE Dep., University of Waterloo, August 2010.)
• Mahdi Cheraghchi, Ali Hormati, Amin Karbasi, Martin Vetterli, Compressed Sensing with Probabilistic Tests: Theory, Design and Application. (IEEE Transactions on Information Theory 57(10), pp 7057-7067, 2011.)
• Chenlu Qiu and Namrata Vaswani, Recursive Sparse Recovery in large but correlated noise. (Proc. Allerton Conf. Commun., Control, Comput., 2011)
• Mauro Mangia, Riccardo Rovatti, Gianluca Setti, Rakeness in the design of Analog-to-Information Conversion of Sparse and Localized Signals. (IEEE Trans. on Circuits and Systems - Part I, 59(5), 1001-1014, May 2012 )
• Zai Yang, Cishen Zhang, and Lihua Xie, Robustly stable signal recovery in compressed sensing with structured matrix perturbation. (IEEE Trans. Signal Processing, Sept. 2012) [See also related conference publication ICASSP12 ]
• Demba Ba, Behtash Babadi, Patrick Purdon, Emery Brown, Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential l1-Minimization. (Advances in Neural Information Processing (NIPS 2012), 25, 2012.)
• Mahdi Cheraghchi, Noise-Resilient Group Testing: Limitations and Constructions. (Discrete Applied Mathematics 161(1-2), pp 81-95, 2013.)
• Taner Ince, Arif Nacaroglu, Nurdal Watsuji, Nonconvex compressed sensing with partially known signal support. (Signal Processing Volume 93, Issue 1, January 2013, Pages 338â��344)
• Zai Yang and Lihua Xie, Continuous Compressed Sensing With a Single or Multiple Measurement Vectors. (IEEE Workshop on Statistical Signal Processing (SSP), pp. 308â��311, June 2014) [See also extended journal paper at arXiv:1405.6585]
• Valerio Cambareri, Riccardo Rovatti, Gianluca Setti, Maximum Entropy Hadamard Sensing of Sparse and Localized Signals. (Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014.)
• Han Guo, Chenlu Qiu and Namrata Vaswani, An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum. (IEEE Trans. on Signal Processing, 62 (16), pp. 4284-4297)
• Han Guo, Chenlu Qiu and Namrata Vaswani, Practical ReProCS for Separating Sparse and Low-dimensional Signal Sequences From Their Sum - Part 1. (IEEE Int. Conf. Acoust., Speech, Signal Process (ICASSP), Florence, Italy, 2014)

• Joel Tropp and Anna Gilbert,
  
  Signal recovery from random measurements via orthogonal matching pursuit.
  (IEEE Trans. on Information Theory, 53(12) pp. 4655-4666, December 2007)
• Shriram Sarvotham, Dror Baron, and Richard Baraniuk,
  
  Sudocodes - Fast measurement and reconstruction of sparse signals.
  (IEEE Int. Symposium on Information Theory (ISIT), Seattle, Washington, July 2006)
• David Donoho and Yaakov Tsaig,
  
  Fast solution of ell-1-norm minimization problems when the solution may be sparse.
  (Stanford University Department of Statistics Technical Report 2006-18, 2006)
• Massimo Fornasier and Holger Rauhut,
  
  Iterative thresholding algorithms.
  (Preprint, 2007)
• Rick Chartrand,
  
  Exact reconstructions of sparse signals via nonconvex minimization.
  (IEEE Signal Proc. Lett., 14(10) pp. 707-710, 2007)
• Mário A. T. Figueiredo, Robert D. Nowak, and Stephen J. Wright,
  
  Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems.
  (IEEE Journal of Selected Topics in Signal Processing: Special Issue on Convex Optimization Methods for Signal Processing, 1(4), pp. 586-598, 2007)
• Seung-Jean Kim, Kwangmoo Koh, Michael Lustig, Stephen Boyd, and Dimitry Gorinevsky,
  
  A method for large-scale ell-1-regularized least squares problems with applications in signal processing and statistics.
  (Preprint, 2007)
• David L. Donoho, Yaakov Tsaig, Iddo Drori, and Jean-Luc Starck,
  
  Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit.
  (Preprint, 2007)
• Thomas Blumensath and Mike E. Davies,
  
  Iterative thresholding for sparse approximations.
  (Preprint, 2007)
• Thomas Blumensath and Mike E. Davies,
  
  Gradient pursuits.
  (IEEE Trans. on Signal Processing, 56(6), pp. 2370 - 2382, June 2008)
• Karen Egiazarian, Alessandro Foi, and Vladimir Katkovnik,
  
  Compressed sensing image reconstruction via recursive spatially adaptive filtering.
  (Preprint, 2007)
• Ingrid Daubechies, Massimo Fornasier, and Ignace Loris,
  
  Accelerated projected gradient method for linear inverse problems with sparsity constraints.
  (Preprint, 2007)
• Massimo Fornasier,
  
  Domain decomposition methods for linear inverse problems with sparsity constraints.
  (Inverse Problems, 23(6), pp. 2505 - 2526, Dec. 2007)
• Ewout van den Berg and Michael Friedlander,
  
  In pursuit of a root.
  (Preprint, 2007)
• Deanna Needell and Roman Vershynin,
  
  Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit.
  (Preprint, 2007)
• Kristan Bredies and Dirk A. Lorenz,
  
  Iterated hard shrinkage for minimization problems with sparsity constraints.
  (Preprint, 2007)
• José Bioucas-Dias and Mário Figueiredo,
  
  A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration.
  (IEEE Trans. on Image Processing, 16(12), pp. 2992 - 3004, Dec. 2007)
• Mark Iwen,
  
  A deterministic sub-linear time sparse Fourier algorithm via non-adaptive compressed sensing methods.
  (Preprint, 2007)
• Elaine T. Hale, Wotao Yin, and Yin Zhang,
  
  A fixed-point continuation method for ell-1 regularized minimization with applications to compressed sensing.
  (Preprint, 2007)
• Petros Boufounos, Marco F. Duarte, and Richard G. Baraniuk,
  
  Sparse signal reconstruction from noisy compressive measurements using cross validation.
  (Proc. IEEE Workshop on Statistical Signal Processing, Madison, Wisconsin, August 2007)
• Wotao Yin, Stanley Osher, Donald Goldfarm, and Jerome Darbon,
  
  Bregman iterative algorithms for ell-1 minimization with applications to compressed sensing.
  (Preprint, 2007)
• Roland Griesse, Dirk A. Lorenz,
  
  A semismooth Newton method for Tikhonov functionals with sparsity constraints.
  (Preprint, 2007)
• Emmanuel Candès, Michael Wakin, and Stephen Boyd,
  
  Enhancing sparsity by reweighted ell-1 minimization.
  (Preprint, 2008)
• Rick Chartrand and Wotao Yin,
  
  Iteratively reweighted algorithms for compressive sensing.
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Sadegh Jokar and Marc E. Pfetsch,
  
  Exact and approximate sparse solutions of underdetermined linear equations.
  (Preprint, 2007)
• Deanna Needell and Roman Vershynin,
  
  Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit.
  (Preprint, 2007)
• Nam H. Nguyen and Trac D. Tran,
  
  The stability of regularized orthogonal mathcing pursuit.
  (Preprint, 2007)
• Ewout van den Berg and Michael P. Friedlander,
  
  Probing the Pareto frontier for basis pursuit solutions.
  (Preprint, January 2008)
• I.F. Gorodnitsky and B.D. Rao,
  
  Sparse signal reconstruction from limited data using FOCUSS: A re-weighted norm minimization algorithm.
  (IEEE Trans. on Signal Processing, 45, pp. 600 - 616, March 1997)
• B.D. Rao and K. Kreutz-Delgado,
  
  An affine scaling methodology for best basis selection.
  (IEEE Trans. on Signal Processing, 47, pp. 187 - 200, January 1999)
• S. F. Cotter, J. Adler, B. D. Rao, K. Kreutz-Delgado,
  
  Forward sequential algorithms for best basis selection.
  (Proc. Vision, Image, and Signal Processing, pp. 235 - 244, October 1999)
• B.D Rao, K. Engan, S.F. Cotter, J. Palmer, K, Kreutz-Delgado,
  
  Subset selection in noise based on diversity measure minimization.
  (IEEE Trans. on Signal Processing, 51(3), pp. 760 - 770, March 2003)
• S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado,
  
  Sparse solutions to linear inverse problems with multiple measurement vectors .
  (IEEE Trans. on Signal Processing, 53(9), pp. 2477 - 2488, July 2005)
• S. D. Howard, A. R. Calderbank, and S. J. Searle,
  
  A fast reconstruction algortihm for deterministic compressive sensing using second order Reed-Muller codes.
  (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008)
• Wei Dai, Olgica Milenkovic,
  
  Subspace pursuit for compressive sensing: Closing the gap between performance and complexity.
  (Preprint, 2008)
• D. Needell, J. A. Tropp,
  
  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples.
  (Preprint, 2008)
• Lorne Applebaum, Stephen Howard, Stephen Searle, and Robert Calderbank,
  
  Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery.
  (Preprint, 2008)
• T. Blumensath, M. E. Davies,
  
  Iterative hard thresholding for compressed sensing.
  (Preprint, 2008)
• T. Blumensath, M. E. Davies,
  
  Stagewise weak gradient pursuits. Part I: Fundamentals and numerical studies.
  (Preprint, 2008)
• T. Blumensath, M. E. Davies,
  
  Stagewise weak gradient pursuits. Part II: Theoretical properties.
  (Preprint, 2008)
• Stéphane Chrétien,
  
  An alternating ell-1 approach to the compressed sensing problem.
  (Preprint, 2008)
• Thong T. Do, Lu Gan, Nam Nguyen, Trac D. Tran,
  
  Sparsity adaptice matching pursuit algorithm for practical compressed sensing.
  (Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 2008)
• Patrick Combettes, Valérie Wajs,
  
  Signal recovery by proximal forward-backward splitting.
  (Multiscale Modeling and Simulation, 4(4), pp. 1168 - 1200, November 2005)
• Caroline Chaux, Patrick Combettes, Jean-Christophe Pesquet, Valérie Wajs,
  
  A variational formulation for frame-based inverse problems.
  (Inverse Problems, 23, pp. 1495 - 1518, June 2007)
• Patrick Combettes, Jean-Christophe Pesquet,
  
  Proximal thresholding algorithm for minimization over orthonormal bases.
  (SIAM Journal on Optimization, 18(4), pp. 1351 - 1376, November 2007)
• Kai Tobias Block, Martin Uecker, and Jens Frahm, Undersampled Radial MRI with Multiple Coils. Iterative Image Reconstruction Using a Total Variation Constraint. (Magnetic Resonance in Medicine, 57(6), pp. 1086-1098, 2007)
• Jianwei Ma,
  
  Compressed sensing by inverse scale space and curvelet thresholding.
  (Applied Mathematics and Computation, 206, pp. 980-988, 2008)
• Ingrid Daubechies, Ronald DeVore, Massimo Fornasier, C. Sinan Güntürk,
  
  Iteratively re-weighted least squares minimization for sparse recovery.
  (Preprint, 2008)
• S. Wright, R. Nowak, M. Figueiredo,
  
  Sparse reconstruction by separable approximation.
  (Preprint, 2008)
• Venkatesh Saligrama, Manqi Zhao,
  
  Thresholded basis pursuit: Quantizing linear programming solutions for optimal support recovery and approximation in compressed sensing.
  (Preprint, 2008)
• Hossein Mohimani, Massoud Babaie-Zadeh, Christian Jutten,
  
  A fast approach for overcomplete sparse decomposition based on smoothed ell-0 norm.
  (Preprint, 2008)
  [See also related conference publications:
  ICA 2007
  ICASSP 2008]
• R. Berinde, P. Indyk, M. Ružić,
  
  Practical near-optimal sparse recovery in the ell-1 norm.
  (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2008)
• F. Malgouyres, T. Zeng,
  
  A predual proximal point algorithm solving a non negative basis pursuit denoising model.
  (International Journal of Computer Vision (IJCV), vol. 83, num.3, pp 294-311, July 2009)
• Sangkyun Lee, Stephen Wright,
  
  Implementing algorithms for signal and image reconstruction on graphical processing units.
  (Preprint, 2008)
• Albert Cohen, Wolfgang Dahmen, Ronald DeVore,
  
  Instance optimal decoding by thresholding in compressed sensing.
  (Preprint, 2008)
• Salman Asif, Justin Romberg,
  
  Streaming measurements in compressive sensing: ell-1 filtering.
  (Preprint, 2008)
• Dror Baron, Shriram Sarvoham, Richard G. Baraniuk,
  
  Bayesian compressive sensing via belief propagation.
  (To appear in IEEE Trans. Signal Processing, 2009)
• Sergio D. Cabrera, J. Gerardo Rosiles, Alejandro E. Brito,
  
  Affine scaling transformation algorithms for harmonic retrieval in a compressive sensing framework.
  (Proc. Wavelets XIII, SPIE, San Diego, California, August 2007)
• Sergio D. Cabrera, Rufino Dominguez, J. Gerardo Rosiles, Javier Vega-Pineda,
  
  Variable-p affine scaling transformation algorithms for improved compressive sensing.
  (Proc. Sensor, Signal, and Info. Proc. Workshop (SenSIP), Sedona, Arizona, May 2008)
• Rufino J. Dom�nguez, Sergio D. Cabrera, J. Gerardo Rosiles, Javier Vega-Pineda,
  
  Reconstruction in compressive sensing using affine scaling transformations with variable-p diversity measure.
  (Proc. IEEE DSP Workshop, Marco Island, Florida, January 2009)
• Hoa V. Pham, Wei Dai, Olgica Milenkovic,
  
  Sublinear compressive sensing reconstruction via belief propagation decoding.
  (Preprint, January 2009)
• Pierre J. Garrigues, Laurent El Ghaoui,
  
  An homotopy algorithm for the lasso with online observations.
  (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2008)
• Suvrit Sra, Joel Tropp,
  
  Row-action methods for compressed sensing
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006)
• Namrata Vaswani,
  
  Analyzing least squares and kalman filtered compressed sensing
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
• Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten,
  
  Bayesian pursuit algorithm for sparse representation
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
• Daniele Angelosante, Georgios B. Giannakis,
  
  RLS-weighted lasso for adaptive estimation of sparse signals
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
• Thomas Blumensath, Mike E. Davies,
  
  Normalised iterative hard thresholding; guaranteed stability and performance
  (Preprint, 2009)
• Mark Iwen,
  
  Combinatorial sublinear-time Fourier algorithms
  (Preprint, 2009)
• A. Dogandžić and K. Qiu,
  
  Variance-component based sparse signal reconstruction and model selection.
  (To appear in IEEE Trans. Signal Processing, vol. 58, 2010)
• Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh,
  
  SPARLS: A low complexity recursive ell1-regularized least squares algorithm.
  (Preprint, January 2009).
• M. Salman Asif, Justin Romberg,
  
  Dynamic updating for sparse time varying signals.
  (CISS 2009, Baltimore, MD)
• Namrata Vaswani, Wei Lu,
  
  Modified-CS: Modifying compressive sensing for problems with partially known support.
  (IEEE Intl. Symp. Info. Theory (ISIT), 2009)
• Rahul Garg, Rohit Khandekar,
  
  Gradient descent with sparsification: An iterative algorithm for sparse recovery with restricted isometry property.
  (ICML 2009, Montreal, Canada)
• Gerlind Plonka, Jianwei Ma,
  
  Curvelet-wavelet regularized split Bregman iteration for compressed sensing.
  (Preprint, June 2009)
• Mark Davenport and Michael Wakin,
  
  Analysis of orthogonal matching pursuit using the restricted isometry property.
  (IEEE Trans. on Information Theory, 56(9), pp. 4395 - 4401, September 2010)
• Yilun Wang, Wotao Yin,
  
  Compressed sensing via iterative support detection.
  (Rice CAAM TR09-30, September 2009)
• Zachary Harmany, Roummel Marcia, Rebecca Willett,
  Sparse Poisson intensity reconstruction algorithms.
  (Proc. IEEE Workshop on Statistical Signal Processing, 2009)
• Ming Gu, Lek-Heng Lim, Cinna Julie Wu,
  
  PARNES: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals.
  (Preprint, 2009)
• W. Yin, S. P. Morgan, J. Yang, Y. Zhang,
  
  Practical compressive sensing with Toeplitz and circulant matrices.
  (Rice University CAAM Technical Report TR10-01, Submitted to VCIP 2010)
  [additional info]
• Entao Liu, V.N. Temlyakov
  
  Orthogonal super greedy algorithm and applications in compressed sensing.
  (Preprint, Jan 2010)
• Wei Lu, Namrata Vaswani,
  
  Modified basis pursuit denoising (Modified-BPDN) for noisy compressive sensing with partially known support.
  (IEEE ICASSP 2010)
• Wei Lu, Namrata Vaswani,
  
  Regularized Modified-BPDN for compressive sensing with partially known support.
  (Preprint, Feb 2010)
• G. Mileounis, B. Babadi, N. Kalouptsidis, V. Tarokh,
  
  An adaptive greedy algorithm with application to sparse NARMA identification.
  (IEEE ICASSP 2010)
• Gerasimos Mileounis, Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh,
  
  An adaptive greedy algorithm with application to nonlinear communications.
  (To appear in the IEEE Trans. on Signal Processing, 2010)
• D. Angelosante, J.-A. Bazerque, G. B. Giannakis,
  
  Online Adaptive Estimation of Sparse Signals: where RLS meets the L1-norm.
  (Accepted to IEEE Trans. on Sign. Proc., 2010)
• K. Qiu, A. Dogandžić,
  
  Double overrelaxation thresholding methods for sparse signal reconstruction.
  (Proc. 44th Annu. Conf. Inform. Sci. Syst., Princeton, NJ, Mar. 2010)
• Eugene Livshitz,
  On the optimality of Orthogonal Greedy Algorithm for M-coherent dictionaries.
  (Preprint, March 2010)
• Atul Divekar, Okan Ersoy, Probabilistic Matching Pursuit for Compressive sensing. (Purdue ECE Technical Report TR-ECE-10-03, 2010)
• Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh, SPARLS: The Sparse RLS Algorithm. (To appear in the IEEE Trans. on Signal Processing, 2010)
• Eugene Livshitz, On efficiency of Orthogonal Matching Pursuit . (Preprint, 2010)
• Zachary T. Harmany, Roummel F. Marcia, and Rebecca M. Willett, This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms -- Theory and Practice. (Submitted to IEEE Transactions on Image Processing)
• Rebecca M. Willett, Zachary T. Harmany, and Roummel F. Marcia, Poisson Image Reconstruction with Total Variation Regularization. (IEEE International Conference on Image Processing, September 2010)
• Zachary T. Harmany, Daniel O. Thompson, Rebecca M. Willett, and Roummel F. Marcia, Gradient Projection for Linearly Constrained Convex Optimization in Sparse Signal Recovery. (IEEE International Conference on Image Processing, September 2010)
• Zvika Ben-Haim, Yonina C. Eldar, and Michael Elad, Coherence-based performance guarantees for estimating a sparse vector under random noise. (to appear in IEEE Trans. Signal Process., 2010)
• Ryota Tomioka and Masashi Sugiyama, Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction. (IEEE Signal Processing Letters, 16(12), pp. 1067 - 1070, 2009) [[Software]]
• Zhiqiang Xu, A remark about orthogonal matching pursuit algorithm. (arXiv:1005.3093)
• Balakrishnan Varadarajan, Sanjeev Khudanpur and Trac Tran, Stepwise Optimal Subspace Pursuit for Improving Sparse Recovery. (IEEE Letters on Signal Processing, 18(1), pp. 27-30, Jan. 2011 ) [http://sites.google.com/site/balakrishnanvaradarajan/pubs/SPL-08761-2010(extended).pdf]
• Avi Septimus and Raphael Steinberg, Compressive Sampling Hardware Reconstruction. (Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on. IEEE, 2010, pp. 3316â��3319.)
• Shisheng Huang, Jubo Zhu, Recovery of sparse signals using OMP and its variants: convergence analysis based on RIP . (Inverse Problems, 2011, 27(3))
• Stephen Becker, E. J. Candès and M. Grant, Templates for convex cone problems with applications to sparse signal recovery. (submitted, September 2010)
• Daniel H. Chae, Parastoo Sadeghi, and Rodney A. Kennedy, Effects of Basis-mismatch in Compressive Sampling of Continuous Sinusoidal Signals. (IEEE International Conference on Future Computer and Communication (ICFCC), 2010 )
• Y. Kopsinis, K. Slavakis, S. Theodoridis, Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted $ell_{1}$ Balls. (IEEE Trans. on Signal Processing, pp. 936-952, Mar. 2011.)
• Jaewook Kang, Heung-No Lee, Kiseon Kim, Message passing aided least square recovery for compressive sensing. (accepted by SPARS '11, Edinburgh, Scotland, UK, Apr., 2011)
• Wei Deng, Wotao Yin, and Yin Zhang, Group Sparse Optimization by Alternating Direction Method. (Technical Report TR11-06, Department of Computational and Applied Mathematics, Rice University, 2011)
• Jiao Wu, Fang Liu, LC Jiao and Xiaodong Wang, Compressive Sensing SAR Image Reconstruction Based on Bayesian Framework and Evolutionary Computation. (IEEE Trans. on Image Processing (in press), doi: 10.1109/TIP.2010.2104159 )
• Zhilin Zhang, Bhaskar D. Rao, Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors. (ICASSP 2011)
• Zhilin Zhang, Bhaskar D. Rao, Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsi. (ICML 2011 Workshop on Structured Sparsity: Learning and Inference)
• Nazim Burak Karahanoglu and Hakan Erdogan, A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery. (Preprint, 2010) [See also related conference publication: ICASSP 2011]
• Amin Khajehnejad, Juhwan Yoo, Animashree Anandkumar ana Babak Hassibi, Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing. (International Symposium on Information Theory, 2011)
• Yuzhe Jin, Bhaskar D. Rao, MultiPass Lasso Algorithms for Sparse Signal Recovery. (ISIT 2011, St. Petersburg, Russia.)
• Adam S. Charles, Pierre Garrigues, Christopher J. Rozell, Analog Sparse Approximation with Applications to Compressed Sensing. (arXiv:1111.4118v1 [math.OC])
• Zai Yang, Cishen Zhang, Jun Deng, and Wenmiao Lu, Orthonormal expansion l1-minimization algorithms for compressed sensing. (IEEE Trans. on Signal Processing, vol. 59, no. 12, pp. 6285--6290, 2011) [Matlab codes]
• Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon, Orthogonal Matching Pursuit with Replacement. (Advances in Neural Information Processing Systems 24 (NIPS 2011), pp. 1215-1223, 2011)
• Gerlind Plonka, Jianwei Ma, Curvelet-wavelet regularized split Bregman iteration for compressed sensing. (International Journal of Wavelets, Multiresolution and Information Processing, 2011, 9(1), 79-110)
• Jaewook Kang, Heung-No Lee, and Kiseon Kim, On Detection-Directed Estimation approach for Noisy Compressive Sensing. (Submitted to IEEE Trans. Signal processing Jan., 2012)
• J. Wang, S. Kwon, and B. Shim, Near optimal bound of orthogonal matching pursuit using restricted isometric constant. (To appear in Eurasip journal on advances in signal processing)
• Yunbin Zhao and Duan Li, Reweighted l1-Minimization for Sparse Solutions to Underdetermined Linear Systems. (Submitted to SIAM J. Optimization)
• Hung-Wei Chen, Chun-Shien Lu, and Soo-Chang Pei, Fast Compressive Sensing Recovery with Transform-based Sampling. (Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations, June 27-30, Edinburgh, UK, 2011.)
• Dirk A. Lorenz, Marc. E. Pfetsch, Andreas M. Tillmann, Solving Basis Pursuit: Subgradient Algorithm, Heuristic Optimality Check, and Solver Comparison. (Optimization Online E-Print ID 2011-07-3100) [http://www.math.nat.tu-bs.de/mo/spear/]
• Jaewook Kang, Heung-No Lee, and Kiseon Kim, Bayesian Hypothesis Test for Sparse Support Recovery using Belief Propagation. ( IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Aug. 2012.)
• Jian Zhang, Debin Zhao, Chen Zhao, Ruiqin Xiong, Siwei Ma, Wen Gao, Compressed Sensing Recovery via Collaborative Sparsity. (Proc. of IEEE Data Compression Conference (DCC), pp. 287-296, Snowbird, Utah, USA, Apr. 2012.)
• Gholami, A., Hosseini, S.M., , A General Framework for Sparsity-based Denoising and Inversion. (IEEE Transactions on Signal Processing. vol. 59, no. 11, pp. 5202-5211.)
• C. Herzet and A. Dremeau, Bayesian Pursuit Algorithms. (EUSIPCO'2010, European Signal Processing conference, Aalborg, Danemark, August 2010) [technical report]
• Kimon Fountoulakis, Jacek Gondzio, Pavel Zhlobich, Matrix-free Interior Point Method for Compressed Sensing Problems. (Technical Report ERGO 12-006, School of Mathematics and Maxwell Institute, The University of Edinburgh, June/2012)
• Entao Liu and Vladimir N. Temlyakov, The Orthogonal Super Greedy Algorithm and Applications in Compressed Sensing. (IEEE Trans. on Information Theory, 58(4), pp. 2040-2047, April 2012)
• K.Fountoulakis, J. Gondzio and P. Zhlobich, Matrix-free interior point method for compressed sensing problems. (Preprint 2012)
• Xueqin Zhou, Xiangchu Feng, and Mingli Jing, Sparse Recovery by Semi-Iterative Hard Thresholding Algorithm. (Mathematical Problems in Engineering, vol. 2013, Article ID 498589, 6 pages, 2013.)
• Mohammad-Khaninezhad M.R., Jafarpour B., Sparse Bayesian inversion for reservoir history matching and uncertainty quantification. (SPE-163656-MS presented at the Society of Petroleum Engineers Reservoir Simulation Symposium, Woodlands, TX, 18-20 February 2013)
• Li L., Jafarpour B. , Effective solution of nonlinear subsurface flow inverse problems in sparse bases. (Inverse Problems, 26(10),105-016 (24pp).)
• Gonzalo H Otazu and Christian Leibold, A corticothalamic circuit model for sound identification in complex scenes.. (PLoS One. 2011;6(9):e24270. doi: 10.1371/journal.pone.0024270. Epub 2011 Sep 13.)
• Dmitry Dovnar, Yury Lebedinsky, Igor Zakharov, New Concept of Image Restoring. (3rd IEEE Benelux Signal Processing Symposium (SPS-2002), Leuven, Belgium, March 21-22, 2002) [See, please other related publications at http://losi.itgo.com]
• Haixiao Liu, Bin Song, Hao Qin, and Zhiliang Qiu, An Adaptive-ADMM Algorithm With Support and Signal Value Detection for Compressed Sensing. (IEEE Trans. on Signal Processing Letters, 20 (4), pp. 315-318, April 2013)
• Mingrui Yang; Frank de Hoog, Coherence and RIP Analysis for Greedy Algorithms in Compressive Sensing. (arxiv:1307.1949)
• Y.B. Zhao, RSP-Based Analysis for Sparsest and Least â��1-Norm Solutions to Underdetermined Linear Systems. (IEEE Trans. on Signal Processing, 61 (2013), pp.)
• Heping Song and Guoli Wang, Sparse Signal Recovery via ECME Thresholding Pursuits. (Mathematical Problems in Engineering, vol. 2012, Article ID 478931, 22 pages, 2012.) [code]
• Mohammad Reza Mohammadi, Emad Fatemizadeh, Mohammad H. Mahoor, Non-negative sparse decomposition based on constrained smoothed â��0 norm. (Signal Processing, 2014 (doi = http://dx.doi.org/10.1016/j.sigpro.2014.01.010))
• Mudassir Masood and Tareq Y. Al-Naffouri, Sparse Reconstruction Using Distribution Agnostic Bayesian Matching Pursuit. (IEEE Transactions on Signal Processing, vol. 61, no. 21, pp. 5298-5309, November 2013)
• S. Kwon, J. Wang, B. Shim, Multipath Matching Pursuit. (to appear in IEEE Trans. Information Theory, 2014)
• Mingli Jing, Xueqin Zhou, Chun Qi, Quasi-Newton Iterative Projection Algorithm for Sparse Recovery. (Neurocomputing, Volume 144, 20 November 2014, Pages 169â��173)
• Norbert Goertz, Chunli Guo, Alex Jung, Mike Davies, Gerhard Doblinger, Iterative Recovery of Dense Signals from Incomplete Measurements. (IEEE Signal Processing Letters, 21 (2014), 9; 1059 - 1063)

• Martin Vetterli, Pina Marziliano, and Thierry Blu,
  
  Sampling signals with finite rate of innovation.
  (IEEE Trans. on Signal Processing, 50(6), pp. 1417-1428, June 2002)
• Irena Maravic and Martin Vetterli,
  
  Sampling and reconstruction of signals with finite rate of innovation in the presence of noise.
  (IEEE Trans. on Signal Processing, 53(8), pp. 2788-2805, August 2005)
• Yue Lu and Minh Do,
  
  A geometrical approach to sampling signals with finite rate of innovation.
  (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, Canada, May 2004)
• Ivana Jovanovic and Baltasar Beferull-Lozano,
  
  Oversampled A/D conversion and error-rate dependence of nonbandlimited signals with finite rate of innovation.
  (IEEE Trans. on Signal Processing, 54(6), pp. 2140-2154 , June 2006)
• Pier Luigi Dragotti, Martin Vetterli, and Thierry Blu,
  
  Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets Strang-Fix.
  (IEEE Trans. on Signal Processing, 55(7), pp. 1741-1757, May 2007)
• P. Shukla and P. L. Dragotti,
  
  Sampling schemes for multidimensional signals with finite rate of innovation.
  (IEEE Trans. on Signal Processing, 55(7), pp. 3670-3686, July 2007)
• Vincent Y. F. Tan and Vivek K Goyal,
  
  Estimating signals with finite rate of innovation from noisy samples: A stochastic algorithm.
  IEEE Trans. on Signal Processing, 56(10), pp. 5135-5146, October 2008
• Julius Kusuma and Vivek K Goyal,
  
  Multichannel sampling of parametric signals with a successive approximation property.
  (IEEE Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006)
• L. Baboulaz and P.L. Dragotti,
  
  Exact feature extraction using finite rate of innovation principles with an application to image super-resolution.
  (IEEE Trans. on Image Processing, 18(2), pp. 281 - 298, February 2009)
• R. Tur, Y. C. Eldar, Z. Friedman
  
  Low Rate Sampling of Pulse Streams with Application to Ultrasound Imaging.
  (Submitted to IEEE Transactions on Signal Processing, Mar. 2010)
• Kfir Gedalyahu, Ronen Tur and Yonina C. Eldar, Multichannel Sampling of Pulse Streams at the Rate of Innovation. (submitted to IEEE Trans. on Signal Processing, Apr. 2010.)
• Jesse Berent and Pier Luigi Dragotti and Thierry Blu, Sampling Piecewise Sinusoidal Signals with Finite Rate of Innovation Methods. (IEEE Trans. on Signal Processing, Vol. 58(2),pp. 613-625, February 2010.)
• H. Akhondi Asl and P.L. Dragotti and L. Baboulaz , Multichannel Sampling of Signals with Finite Rate of Innovation,. (IEEE Signal Processing Letter, Vo. 17(8), pp. 762-765, August 2010.)
• Tomer Michaeli and Yonina C. Eldar, Xampling at the rate of innovation. (to appear in IEEE Transactions on Signal Processing)

• J. Haupt, R. Castro, and R. Nowak,
  
  Distilled sensing: selective sampling for sparse signal recovery.
  (to appear in Proc. 12th Conference on Artificial Intelligence and Statistics, Clearwater Beach, FL, April 2009)
• A. Aldroubi, H. Wanf and K. Zarringhalam,
  
  Sequential Adaptive compressed sampling via Huffman codes.
  (Preprint 2009)
• M. A. Iwen & A. H. Tewfik, Adaptive Group Testing Strategies for Target Detection and Localization in Noisy Environments. (Preprint, 2010)

---

 

This work is licensed under a Creative
Commons Attribution-Noncommercial-Share Alike 3.0 Unported
License.