Overview

The dogma of signal processing maintains that a signal must be sampled at a rate at least twice its highest frequency in order to be represented without error. However, in practice, we often compress the data soon after sensing, trading off signal representation complexity (bits) for some error (consider JPEG image compression in digital cameras, for example). Clearly, this is wasteful of valuable sensing resources. Over the past few years, a new theory of "compressive sensing" has begun to emerge, in which the signal is sampled (and simultaneously compressed) at a greatly reduced rate.

Compressive sensing is also referred to in the literature by the terms: compressed sensing, compressive sampling, and sketching/heavy-hitters.

To contribute a reference or suggest a correction, please email md at rice dot edu.

• 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)
• Marco Duarte, Mark Davenport, Dharmpal Takhar, Jason Laska, Ting Sun, Kevin Kelly, and Richard Baraniuk, Single-pixel imaging via compressive sampling. (IEEE Signal Processing Magazine, 25(2), pp. 83 - 91, March 2008)
• 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. (To appear in Constructive Approximation) [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 Schass, 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. (Mathematical Notes, 82(5-6), pp. 748 - 755, Nov. 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. (Preprint, 2008)
• 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-—59, 2008)
• T. Tony Cai, Guangwu Xu, and Jun Zhang, On recovery of sparse signals via ell-1 minimization. (Preprint, 2008)

• Gabriel Peyré, Best basis compressed sensing. (Preprint, 2006) [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. (Preprint, 2007)
• 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, Cross validation in compressed sensing via the Johnson Lindenstrauss lemma. (Preprint, 2008)
• Petros Boufounos and Richard G. Baraniuk, 1-Bit compressive sensing. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, 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 Yulmaz, Stable sparse approximation via nonconvex optimization. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Yonina C. Eldar, Compressed sensing of analog signals. (Preprint, 2008)

Multi-Sensor and Distributed Compressive Sensing

• Dror Baron, Michael Wakin, Marco Duarte, Shriram Sarvotham, and Richard Baraniuk, Distributed compressed sensing. (Preprint, 2005) [See also related technical report and conference publications: Allerton 2005, Asilomar 2005, NIPS 2005, IPSN 2006]
• Waheed Bajwa, Jarvis Haupt, Akbar Sayeed, and Rob Nowak, Compressive wireless sensing. (Int. Conf. on Information Processing in Sensor Networks (IPSN), Nashville, Tennessee, April 2006)
• Michael Rabbat, Jarvis Haupt, Aarti Singh, and Rob Nowak, Decentralized compression and predistribution via randomized gossiping. (Int. Conf. on Information Processing in Sensor Networks (IPSN), Nashville, Tennessee, April 2006)
• Massimo Fornasier and Holger Rauhut, Recovery algorithms for vector valued data with joint sparsity constraints. (SIAM Journal on Numerical Analysis, 46(2) pp. 577-613, 2008)
• Rémi Gribonval, Holger Rauhut, Karin Schnass, and Pierre Vandergheynst, Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms. (Preprint, 2007) [See also related conference publication: ICASSP 2007]
• Wei Wang, Minos Garofalakis, and Kannan Ramchandran, Distributed sparse random projections for refinable approximation. (Int. Conf. on Information Processing in Sensor Networks (IPSN), Cambridge, Massachusetts, April 2007)
• W. Bajwa, J. Haupt, A. Sayeed and R. Nowak, Joint source-channel communication for distributed estimation in sensor networks. (IEEE Trans. on Information Theory, 53(10) pp. 3629-3653, October 2007)
• Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, Sensing capacity of sensor networks: Fundamental tradeoffs of SNR, sparsity, and sensing diversity. (Information Theory and Applications Workshop, January 2007)
• Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, On sensing capacity of sensor networks for the class of linear observation, fixed SNR models. (Preprint, 2007)
• Moshe Mishali and Yonina C. Eldar, Reduce and boost: Recovering arbitrary sets of jointly sparse vectors. (Preprint, February 2008)
• Volkan Cevher, Marco Duarte, and Richard Baraniuk, Distributed target localization via spatial sparsity. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008)
• Jong Chul Ye and Su Yeon Lee, Non-iterative exact inverse scattering using simultanous orthogonal matching pursuit (S-OMP). (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, Fundamental limits on sensing capacity for sensor networks and compressed sensing. (Preprint, 2008)
• Yang Xiao, Underwater acoustic sensor networks. (Excerpt, Auerbach Publications, 2008)

• 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)
• Marco Duarte, Michael Wakin, and Richard Baraniuk, Fast reconstruction of piecewise smooth signals from random projections. (SPARS Workshop, November 2005)
• Chinh La and Minh Do, Signal reconstruction using sparse tree representations. (SPIE Wavelets XI, San Diego, California, September 2005)
• 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, 2007)
• Rick Chartrand and Wotao Yin, Iteratively reweighted algorithms for compressive sensing. (Preprint, 2007)
• 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 and Olgica Milenkovic, Subspace pursuit for compressive sensing: Closing the gap between performance and complexity. (Preprint, 2008)
• D. Needell and 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 and M. E. Davies, Iterative hard thresholding for compressed sensing. (Preprint, 2008)
• T. Blumensath and M. E. Davies, Stagewise weak gradient pursuits. Part I: Fundamentals and numerical studies. (Preprint, 2008)
• T. Blumensath and 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, and 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 and 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, and Valérie Wajs, A variational formulation for frame-based inverse problems. (Inverse Problems, 23, pp. 1495 - 1518, June 2007)
• Patrick Combettes and Jean-Christophe Pesquet, Proximal thresholding algorithm for minimization over orthonormal bases. (SIAM Journal on Optimization, 18(4), pp. 1351 - 1376, November 2007)
• Jianwei Ma, Compressed sensing by inverse scale space and curvelet thresholding. (Preprint, 2008)
• Ingrid Daubechies, Ronald DeVore, Massimo Fornasier, and C. Sinan Güntürk, Iteratively re-weighted least squares minimization for sparse recovery. (Preprint, 2008)

• Emmanuel Candès and Terence Tao, Decoding by linear programming. (IEEE Trans. on Information Theory, 51(12), pp. 4203 - 4215, December 2005)
• Emmanuel Candès and Terence Tao, Error correction via linear programming. (Preprint, 2005)
• Mark Rudelson and Roman Vershynin, Geometric approach to error correcting codes and reconstruction of signals. (Int. Mathematical Research Notices, 64, pp. 4019 - 4041, 2005)
• Emmanuel Candès and Justin Romberg, Encoding the ell-p ball from limited measurements. (IEEE Data Compression Conference (DCC), Snowbird, UT, 2006)
• Shriram Sarvotham, Dror Baron, and Richard Baraniuk, Measurements vs. bits: Compressed sensing meets information theory. (Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2006)
• Emmanuel Candès and Paige Randall, Highly robust error correction by convex programming. (Preprint, 2006)
• Petros Boufounos and Richard Baraniuk, Quantization of sparse representations. (Rice ECE Department Technical Report TREE 0701 - Summary appears in Data Compression Conference (DCC), Snowbird, Utah, March 2007)
• Martin Wainwright, Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting. (IEEE Int. Symposium on Information Theory (ISIT), Nice, France, June 2007)
• Mehmet Akcakaya and Vahid Tarokh, A frame construction and a universal distortion bound for sparse representations. (IEEE Int. Symposium on Information Theory (ISIT), Nice, France, June 2007)
• Rick Chartrand, Nonconvex compressed sensing and error correction. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 2007)
• Galen Reeves, Sparse signal sampling using noisy linear projections. (Master's Thesis, December 2007)
• Galen Reeves and Michael Gastpar, Sampling bounds for sparse support recovery in the presence of noise. (Preprint, January 2008)
• Mehmet Akcakaya and Vahid Tarokh, Shannon theoretic limits on noisy compressed sensing. (Preprint, November 2007)
• Wei Dai and Olgica Milenkovic, Sparse weighted Euclidean superimposed coding for integer compressed sensing. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008)
• Wei Dai and Olgica Milenkovic, Weighted Euclidean superimposed codes for integer compressed sensing. (IEEE Info. Theory Workshop (ITW), Porto, Portugal, May 2008)
• Alyson K. Fletcher, Sundeep Rangan, Vivek K Goyal, and Kannan Ramchandran, Denoising by sparse approximation: Error bounds based on rate-distortion theory. (EURASIP J. Applied Signal Processing, 2006, Article ID 26318.)
• Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal, On the Rate-Distortion Performance of Compressed Sensing. (IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 2007)
• Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal, Rate-distortion bounds for sparse approximation. (IEEE Statistical Signal Processing Workshop (SSP), Madison, Wisconsin, August 2007)

High-Dimensional Geometry

• David Donoho, High-dimensional centrally-symmetric polytopes with neighborliness proportional to dimension. (Disc. Comput. Geometry, 35(4) pp. 617-652, 2006)
• David Donoho, Neighborly polytopes and sparse solutions of undetermined linear equations. (Preprint, 2005)
• David Donoho and Jared Tanner, Neighborliness of randomly-projected simplices in high dimensions. (Proc. National Academy of Sciences, 102(27), pp. 9452-9457, 2005)
• David Donoho and Jared Tanner, Counting faces of randomly-projected polytopes when the projection radically lowers dimension. (Submitted to Journal of the AMS)
• Richard Baraniuk and Michael Wakin, Random projections of smooth manifolds. (To appear in Foundations of Computational Mathematics) [See also related conference publication: ICASSP 2006]
• Venkatesan Guruswami, James R. Lee, and Alexander Razborov, Almost Euclidean subspaces of ell-1-N via expander codes. (Electronic Colloquium on Computational Complexity, Report TR07-089, September, 2007)
• J. Haupt and R. Nowak, A generalized restricted isometry property. (University of Wisconsin Madison Technical Report ECE-07-1, May 2007)

Ell-1 Norm Minimization

• David Donoho, For most large underdetermined systems of linear equations, the minimal ell-1 norm solution is also the sparsest solution. (Communications on Pure and Applied Mathematics, 59(6), pp. 797-829, June 2006)
• David Donoho, For most large underdetermined systems of linear equations, the minimal ell-1 norm near-solution approximates the sparsest near-solution. (Communications on Pure and Applied Mathematics, 59(7), pp. 907-934, July 2006)
• David Donoho and Jared Tanner, Sparse nonnegative solutions of underdetermined linear equations by linear programming. (Proc. National Academy of Sciences, 102(27), pp.9446-9451, 2005)
• David Donoho and Jared Tanner, Thresholds for the recovery of sparse solutions via ell-1 minimization. (Conf. on Information Sciences and Systems, March 2006)
• Rémi Gribonval and Morten Nielsen, Highly sparse representations from dictionaries are unique and independent of the sparseness measure. (Applied and Computational Harmonic Analysis, 22(3), pp. 335-355, May 2007) [See also related conference publication: ICA 2004]
• Rémi Gribonval, Rosa Maria Figueras I Ventura, and Pierre Vandergheynst, A simple test to check the optimality of a sparse signal approximation. (EURASIP Signal Processing, special issue on Sparse Approximations in Signal and Image Processing, 86(3), pp. 496-510, March 2006) [See also related conference publication: ICASSP 2005]

Statistical Signal Processing

• Marco Duarte, Mark Davenport, Michael Wakin, and Richard Baraniuk, Sparse signal detection from incoherent projections. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006)
• Mark Davenport, Michael Wakin, and Richard Baraniuk, Detection and estimation with compressive measurements. (Rice ECE Department Technical Report TREE 0610, November 2006)
• Jarvis Haupt, Rui Castro, Robert Nowak, Gerald Fudge, and Alex Yeh, Compressive sampling for signal classification. (Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 2006)
• Mark Davenport, Marco Duarte, Michael Wakin, Jason Laska, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, The smashed filter for compressive classification and target recognition. (Computational Imaging V at SPIE Electronic Imaging, San Jose, California, January 2007)
• Jarvis Haupt and Robert Nowak, Compressive sampling for signal detection. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 2007)
• Frank Boyle, Jarvis Haupt, Gerald Fudge, and Robert Nowak, Detecting signal structure from randomly-sampled data. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007)
• Marco Duarte, Mark Davenport, Michael Wakin, Jason Laska, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, Multiscale random projections for compressive classification. (IEEE Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007)

Machine Learning

• Michael Elad, Optimized projections for compressed sensing. (IEEE Trans. on Signal Processing, 55(12), pp. 5695-5702, December 2007)
• Julien Mairal, Guillermo Sapiro, and Michael Elad, Multiscale sparse image representation with learned dictionaries. (Preprint, 2007)
• John Wright, Arvind Ganesh, Allen Yang, and Yi Ma, Robust face recognition via sparse representation. (Preprint, 2007)
• Allen Yang, John Wright, Yi Ma, and Shankar Sastry, Feature selection in face recognition: A sparse representation perspective. (Preprint, 2007)
• Chinmay Hegde, Michael Wakin, and Richard Baraniuk, Random projections for manifold learning. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2007) [See also related technical report]
• D.P. Wipf and B.D. Rao, Sparse bayesian learning for basis selection . (IEEE Trans. on Signal Processing, Special Issue on Machine Learning Methods in Signal Processing, 52, pp. 2153 - 2164, August 2004)
• Julio Martin Duarte-Carvajalino and Guillermo Sapiro, Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. (Preprint, 2008)
• J. F. Gemmeke and B. Cranen, Noise reduction through compressed sensing. (Interspeech 2008, Brisbane, Australia, September 2008)
• J. F. Gemmeke and B. Cranen, Using sparse representations for missing data imputation in noise robust speech recognition . (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008)
• J. F. Gemmeke and B. Cranen, Noise robust digit recognition using sparse representations. (ISCA Tutorial and Research Workshop (ITRW) on Speech Analysis and Processing for Knowledge Discovery, Aalborg, Denamrk, June 2008)
• Julien Mairal, Fracis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman, Discriminative learned dictionaries for local image analysis. (IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008)
• Fernando Rodriguez and Guillermo Sapiro, Sparse representations for image classification: Learning discriminative and reconstructive non-parametric dictionaries. (Preprint, 2008)

Bayesian Methods

• Mauricio Sacchi, Tadeusz Ulrych, and Colin Walker, Interpolation and extrapolation using a high-resolution discrete Fourier transform. (IEEE Trans. on Signal Processing, 46(1) pp. 31 - 38, January 1998)
• Shriram Sarvotham, Dror Baron, and Richard Baraniuk, Compressed sensing reconstruction via belief propagation. (Rice ECE Department Technical Report TREE 0601, 2006)
• Shihao Ji, Ya Xue, and Lawrence Carin, Bayesian compressive sensing. (IEEE Trans. on Signal Processing, 56(6) pp. 2346 - 2356, June 2008) [See also related conference publication: ICML 2007]
• David Wipf, Jason Palmer, Bhaskar Rao, and Kenneth Kreutz-Delgado, Performance evaluation of latent variable models with sparse priors. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, May 2007)
• Shihao Ji, David Dunson, and Lawrence Carin, Multi-task compressive sensing. (Preprint, 2007)
• D.P. Wipf, J.A. Palmer, and B.D. Rao, Perspectives on Sparse Bayesian Learning. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2004)
• D. Wipf and B. D. Rao, An empirical bayesian strategy for solving the simultaneous sparse approximation problem. (IEEE Trans. on Signal Processing, 55(7), pp. 3704 - 3716, July 2007)
• R.M. Castro, J. Haupt, R. Nowak, and G.M. Raz, Finding needles in noisy haystacks. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Yuting Qi, Dehong Liu, David Dunson, and Lawrence Carin, Bayesian multi-task compressive sensing with dirichlet process priors. (Preprint, 2008)
• Matthias W. Seeger and Hannes Nickish, Compressed sensing and bayesian experimental design. (Int. Conf. on Machine Learning (ICML), Helsinki, Finland, July 2008)

Finite Rate of Innovation

• 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. (Submitted to IEEE Trans. Signal Processing)
• Julius Kusuma and Vivek K Goyal, Multichannel sampling of parametric signals with a successive approximation Ppoperty. (IEEE Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006)

Multi-band Signals

• Moshe Mishali and Yonina C. Eldar, Blind multi-band signal reconstruction: compressed sensing for analog signals. (Preprint, 2007)

Lossy Compression

• 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)

• Graham Cormode and S. Muthukrishnan, Towards an algorithmic theory of compressed sensing. (Technical Peport DIMACS TR 2005-25, 2005)
• Graham Cormode and S. Muthukrishnan, Combinatorial algorithms for compressed sensing. (Technical Report DIMACS TR 2005-40, 2005)
• S. Muthukrishnan, Some algorithmic problems and results in compressed sensing. (Preprint, 2006)
• Anna Gilbert, Martin Strauss, Joel Tropp, and Roman Vershynin, One sketch for all: Fast algorithms for compressed sensing. (Symp. on Theory of Computing (STOC), San Diego, California, June, 2007)

Random Sampling

• Anna Gilbert, Sudipto Guha, Piotr Indyk, S. Muthukrishnan, and Martin Strauss, Near-optimal sparse Fourier representations via sampling. (ACM Symposium on Theory of Computing (STOC), 2002)
• Anna Gilbert, S. Muthukrishnan, and M. Strauss, Improved time bounds for near-optimal sparse Fourier representation via sampling. (SPIE Wavelets XI, San Diego, California, September 2005)
• Holger Rauhut, Random sampling of sparse trigonometric polynomials. (Applied and Computational Harmonic Analysis, 22(1), pp. 16-42, Jan. 2007)
• Stefan Kunis and Holger Rauhut, Random sampling of sparse trigonometric polynomials II - Orthogonal matching pursuit versus basis pursuit. (Preprint, 2006)
• Holger Rauhut, Stability results for random sampling of sparse trigonometric polynomials. (Preprint, 2006)

Histogram Maintenance

• Nitin Thaper, Sudipto Guha, Piotr Indyk, and Nick Koudas, Dynamic multidimensional histograms. (SIGMOD 2002, Madison, Wisconson, June 2002)
• Anna Gilbert, Sudipto Guha, Piotr Indyk, Yannis Kotidis, S. Muthukrishnan, and Martin J. Strauss, Fast small-space algorithms for approximate histogram maintenance. (Symp. on Theory of Computing (STOC), Montréal, Canada, May 2002)

Dimension Reduction and Embeddings

• Anna Gilbert, Martin Strauss, Joel Tropp, and Roman Vershynin, Sublinear, Small-space approximation of compressible signals and uniform algorithmic embeddings. (Preprint, 2005) [See Vershynin's discussion of this paper here]
• Anna Gilbert, Martin Strauss, Joel Tropp, and Roman Vershynin, Algorithmic linear dimension reduction in the ell-1 norm for sparse vectors. (Preprint, 2006) [See also related conference publication: Allerton 2006]

• Michael Wakin, Jason Laska, Marco Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, An architecture for compressive imaging. (Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006)
• Michael Wakin, Jason Laska, Marco Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk, Compressive imaging for video representation and coding. (Proc. Picture Coding Symposium (PCS), Beijing, China, April 2006)
• Dharmpal Takhar, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Shriram Sarvotham, Kevin Kelly, and Richard Baraniuk, A new compressive imaging camera architecture using optical-domain compression. (Computational Imaging IV at SPIE Electronic Imaging, San Jose, California, January 2006)
• J. Haupt and R. Nowak, Compressive sampling vs conventional imaging. (Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006)
• Lu Gan, Block compressed sensing of natural images. (Conf. on Digital Signal Processing (DSP), Cardiff, UK, July 2007)
• Ray Maleh and Anna Gilbert, Multichannel image estimation via simultaneous orthogonal matching pursuit. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007)
• Ray Maleh, Anna Gilbert, and Martin Strauss, Sparse gradient image reconstruction done faster. (IEEE Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007)
• Karen Egiazarian, Alessandro Foi, and Vladimir Katkovnik, Compressed sensing image reconstruction via recursive spatially adaptive filtering. (IEEE Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007)
• Lu Gan, Thong Do, Trac D. Tran, Fast compressive imaging using scrambled block Hadamard ensemble. (Preprint, 2008)
• V. Stankovic, L. Stankovic, and S. Cheng, Compressive video sampling. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008)
• Roummel Marcia and Rebecca Willett, Compressive coded aperture video reconstruction. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008) [See also related conference publication: ICASSP 2008]

Medical Imaging

• Michael Lustig, David Donoho, and John M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging. (Magnetic Resonance in Medicine, 58(6) pp. 1182 - 1195, December 2007) [See also related conference publication: ISMRM 2006, SPARS 2005, ISMRM 2005]
• M. Lustig, J. M. Santos, D. L. Donoho, and J. M. Pauly, k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity. (ISMRM, Seattle, Washington, May 2006)
• Hong Jung, Jong Chul Ye, and Eung Yeop Kim, Improved k-t BLASK and k-t SENSE using FOCUSS. (Phys. Med. Biol., 52 pp. 3201 - 3226, 2007)
• Jong Chul Ye, Compressed sensing shape estimation of star-shaped objects in Fourier imaging. (Preprint, 2007)
• Joshua Trzasko, Armando Manduca, and Eric Borisch, Highly undersampled magnetic resonance image reconstruction via homotopic ell-0-minimization. (Preprint, 2007) [See also related conference publication: SSP 2007]
• I.F. Gorodnitsky, J. George and B.D. Rao, Neuromagnetic source imaging with FOCUSS: A recursive weighted minimum norm algorithm . (Electrocephalography and Clinical Neurophysiology, 95, pp. 231 - 251, 1995)
• Simon Hu, Michael Lustig, Albert P. Chen, Jason Crane, Adam Kerr, Douglas A.C. Kelley, Ralph Hurd, John Kurhanewicz, Sarah J. Nelsona, John M. Pauly and Daniel B. Vigneron, Compressed sensing for resolution enhancement of hyperpolarized 13C flyback 3D-MRSI. (Journal of Magnetic Resonance, 192(2), pp. 258 - 264, June 2008)
• T. Cukur, M. Lustig, and D.G. Nishimura, Improving non-contrast-enhanced steady-state free precession angiography with compressed sensing. (Preprint, 2008)
• Hong Jung, Kyunghyun Sung, Krishna S. Nayak, Eung Yeop Kim, and Jong Chul Ye, k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI. (To appear in Magnetic Resonance in Medicine, 2008)

Analog-to-Information Conversion

• Joel Tropp, Michael Wakin, Marco Duarte, Dror Baron, and Richard Baraniuk, Random filters for compressive sampling and reconstruction. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006)
• Sami Kirolos, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Tamer Ragheb, Yehia Massoud, and Richard Baraniuk, Analog-to-information conversion via random demodulation. (IEEE Dallas Circuits and Systems Workshop (DCAS), Dallas, Texas, 2006)
• Jason Laska, Sami Kirolos, Yehia Massoud, Richard Baraniuk, Anna Gilbert, Mark Iwen, and Martin Strauss, Random sampling for analog-to-information conversion of wideband signals. (IEEE Dallas Circuits and Systems Workshop (DCAS), Dallas, Texas, 2006)
• Jason Laska, Sami Kirolos, Marco Duarte, Tamer Ragheb, Richard Baraniuk, and Yehia Massoud, Theory and implementation of an analog-to-information converter using random demodulation. (IEEE Int. Symp. on Circuits and Systems (ISCAS), New Orleans, Louisiana, 2007)
• Tamer Ragheb, Sami Kirolos, Jason Laska, Anna Gilbert, Martin Strauss, Richard Baraniuk, and Yehia Massoud, Implementation models for analog-to-information conversion via random sampling. (Midwest Symposium on Circuits and Systems (MWSCAS), 2007)
• Petros Boufounos and Richard G. Baraniuk, Sigma delta quantization for compressive sensing. (Preprint, 2007)

Biosensing

• Mona Sheikh, Olgica Milenkovic, and Richard Baraniuk, Compressed sensing DNA microarrays. (Rice ECE Department Technical Report TREE 0706, May 2007)
• Mona Sheikh, Shriram Sarvotham, Olgica Milenkovic, and Richard Baraniuk, DNA array decoding from nonlinear measurements by beleif propagation. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007)
• Mona Sheikh, Olgica Milenkovic, and Richard Baraniuk, Designing compressive sensing DNA microarrays. (IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), St. Thomas, U.S. Virgin Islands, December 2007)

Geophysical Data Analysis

• Tim Lin and Felix. J. Herrmann, Compressed wavefield extrapolation. (To appear in Geophysics, 2007) [See also related conference publication: SEG 2007]
• Felix J. Herrmann, Deli Wang, Gilles Hennenfent and Peyman Moghaddam, Curvelet-based seismic data processing: a multiscale and nonlinear approach. (To appear in Geophysics, 2007)
• Felix J. Herrmann and Gilles Hennenfent, Non-parametric seismic data recovery with curvelet frames. (UBC Earth & Ocean Sciences Department Technical Report TR-2007-1, 2007)
• Gilles Hennenfent and Felix J. Herrmann, Curvelet reconstruction with sparsity-promoting inversion: successes and challenges. (EAGE 2007)
• Gilles Hennenfent and Felix J. Herrmann, Irregular sampling: from aliasing to noise. (EAGE 2007)
• Felix J. Herrmann, Deli Wang, and Gilles Hennenfent, Multiple prediction from incomplete data with the focused curvelet transform. (SEG 2007)
• Challa Sastry, Gilles Hennenfent, and Felix J. Herrmann, Signal reconstruction from incomplete and misplaced measurements. (EAGE 2007)
• Felix J. Herrmann, Surface related multiple prediction from incomplete data. (EAGE 2007)
• Gilles Hennenfent and Felix J. Herrmann, Simply denoise: wavefield reconstruction via jittered undersampling. (Geophysics, 2008)

Hyperspectral Imaging

• Rebecca Willett, Michael Gehm, and David Brady, Multiscale reconstruction for computational spectral imaging. (Computational Imaging V at SPIE Electronic Imaging, San Jose, California, January 2007)

Compressive Radar

• Richard Baraniuk and Philippe Steeghs, Compressive radar imaging. (IEEE Radar Conference, Waltham, Massachusetts, April 2007)
• Sujit Bhattacharya, Thomas Blumensath, Bernard Mulgrew, and Mike Davies, Fast encoding of synthetic aperture radar raw data using compressed sensing. (IEEE Workshop on Statistical Signal Processing, Madison, Wisconsin, August 2007)
• Matthew Herman and Thomas Strohmer, High-resolution radar via compressed sensing. (Preprint, 2007)

Astronomy

• J. Bobin, J.-L. Starck, and R. Ottensamer, Compressed sensing in astronomy. (Preprint, 2008)

Communications

• S.F. Cotter and B.D. Rao, Sparse channel estimation via matching pursuit with application to equalization. (IEEE Trans. on Communications, 50(3), March 2002)
• Georg Taubŏck and Franz Hlawatsch, A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Waheed U. Bajwa, Jarvis Haupt, Gil Raz, and Robert Nowak, Compressed channel sensing. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008)

Surface metrology

• Jianwei Ma, Compressed sensing for surface metrology. (Preprint, 2008)

Spectrum analysis

• Pierre Borgnat and Patrick Flandrin, Time-frequency localization from sparsity constraints. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)

• l1-Magic
• SparseLab
• GPSR
• ell-1 LS: Simple Matlab Solver for ell-1-Regularized Least Squares Problems
• sparsify
• MPTK: Matching Pursuit Toolkit [See also related conference publication: ICASSP 2006]
• Bayesian Compressive Sensing
• SPGL1: A solver for large scale sparse reconstruction
• sparseMRI
• FPC
• Chaining Pursuit
• Regularized OMP
• SPARCO: A toolbox for testing sparse reconstruction algorithms [See also related technical report]
• TwIST
• Compressed Sensing Codes
• Fast CS using SRM

• IEEE International Conference on Acoustics, Speech, and Signal Processing (April, 2008)
• Conference on Information Sciences and Systems (March, 2008)
• Information Theory and Applications Workshop (January, 2008)
• IEEE Statistical Signal Processing Workshop (August, 2007)
• MADALGO Summer School on Data Stream Algorithms (August, 2007)
• 2007 von Neumann Symposium on Sparse Representations and High-Dimensional Geometry (July, 2007)
• IMA New Directions Short Course: Compressive Sampling and Frontiers in Signal Processing (June, 2007)
• IPAM Short Course on Sparse Representations and High-Dimensional Geometry (June, 2007)
• IEEE International Conference on Acoustics, Speech, and Signal Processing (April, 2007)
• IITK Data Streams Workshop (December, 2006)
• Sparse Approximation Workshop (November, 2006)
• Signal Processing with Adaptative Sparse Structured Representations (November, 2005)
• Dagstuhl Workshop on Sublinear Algorithms (July, 2005)

• Richard Baraniuk, Justin Romberg, and Michael Wakin, Tutorial on compressive sensing (2008 Information Theory and Applications Workshop)
• Petros Boufounos, Justin Romberg and Richard Baraniuk, Compressive sensing - Theory and applications (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)

Blogs

• Nuit Blanche
• Geomblog
• What's new?

Other Related Links

• Preprints in sparse recovery / Summary of properties of random matrices
• Resources on Geophysical Data Reconstruction and Inversion using Sparsity Norms