Compressive Sensing Resources

• References and Software
• Research at Rice

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.As the compressive sensing research community continues to expand rapidly, it behooves us to heed Shannon's advice.Compressive sensing is also referred to in the literature by the terms: compressed sensing, compressive sampling, and sketching/heavy-hitters.Submitting a ResourceTo submit a new or corrected paper for this listing, please complete the form at dsp.rice.edu/cs/submit. To submit a resource that isn't a paper, please email 

---

Tutorials and Reviews

• 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, Compressed Sensing: Theory and Applications. (Preprint)
• See below for tutorial talks on compressive sensing.

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)

Extensions of Compressive Sensing

• 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 -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 S. Hosseini, Derivative Compressive Sampling and its Application to Inverse Problems and Imaging. (M.A.Sc. Thesis, ECE Dep., University of Waterloo, August 2010.)

Multi-Sensor and Distributed Compressive Sensing

• Dror Baron, Marco F. Duarte, Michael B. Wakin, Shriram Sarvotham, and Richard G. Baraniuk, Distributed compressive 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)
• Shoulie Xie, Susanto Rahardja, Zhengguo Li, Wyner-Ziv Image Coding from Random Projections. (IEEE Intl Conf Multimedia & Expo (ICME’07), Beijing, China, 2007)
• Moshe Mishali and Yonina C. Eldar, Reduce and boost: Recovering arbitrary sets of jointly sparse vectors. (IEEE Trans. on Signal Processing, 56(10), pp. 4692-4702, October 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)
• Yang Xiao, Underwater acoustic sensor networks. (Excerpt, Auerbach Publications, 2008)
• Marco Duarte, Shriram Sarvotham, Dror Baron, Michael Wakin, and Richard Baraniuk, Performance limits for jointly sparse signals via graphical models. (Sensor, Signal and Info. Proc. Workshop (SenSIP), Sedona, Arizona, May 2008) [See also related technical report]
• Volkan Cevher, Ali Gurbuz, James McClellan, and Rama Chellappa, Compressive wireless arrays for bearing estimation of sparse sources in angle domain. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Ali Gurbuz, James McClellan, and Volkan Cevher, A compressive beamforming method. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Tao Wan, Nishan Canagarajah, and Alin Achim, Compressive image fusion. (IEEE ICIP 2008, San Diego, CA, Oct., 2008)
• Li-Wei Kang and Chun-Shien Lu, Distributed compressive video sensing (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
• Yonina C. Eldar, Holger Rauhut, Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation. (Preprint, 2009)
• G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer and M. Zorzi, On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks. (Information Theory and Applications Workshop (ITA 2009), San Diego, CA)
• Jia Meng, Husheng Li, and Zhu Han, Sparse Event Detection in Wireless Sensor Networks using Compressive Sensing. (CISS 2009, Baltimore, MD)
• Ewout van den Berg, Michael P. Friedlander, Joint-sparse recovery from multiple measurements. (Preprint, 2009)
• Yasamin Mostofi, Pradeep Sen, Compressive Cooperative Sensing and Mapping in Mobile Networks. (Proceedings of American Control Conference (ACC), Page(s):3397 - 3404, June 2009)
• Riccardo Masiero, Giorgio Quer, Michele Rossi, Michele Zorzi, A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks. (The International Workshop on Scalable Ad Hoc and Sensor Networks (SASN'09), Saint Petersburg, Russia, Oct. 2009)
• Marco F. Duarte, Richard G. Baraniuk, Kronecker Compressive Sensing. (Preprint, 2009)
• Benjamin Miller, Joel Goodman, Keith Forsythe, John Sun, Vivek Goyal, A multi-sensor compressed sensing receiver: Performance bounds and simulated results. (Forty-Third Asilomar Conference on Signals and Systems, pp. 1571-1575, Nov. 2009)
• Riccardo Masiero, Giorgio Quer, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi, Data Acquisition through joint Compressive Sensing and Principal Component Analysis. (IEEE Globecom, Nov.-Dec. 2009)
• Mohammadreza Mahmudimanesh, Abdelmajid Khelil, Nasser Yazdani, Map-Based Compressive Sensing Model for Wireless Sensor Network Architecture, A Starting Point. (First International Workshop on Wireless Sensor Networks Architectures, Simulation and Programming (WASP), pp. 75-84, 2009)
• Mohammadreza Mahmudimanesh, Abdelmajid Khelil and Neeraj Suri, Reordering for Better Compressibility: Efficient Spatial Sampling in Wireless Sensor Networks. (The Third IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC), 2010.)
• Jia Meng, Wotao Yin, Husheng Li, Ekram Hossain, and Zhu Han, Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks. (To appear in IEEE JSAC Special Issue on Cognitive Radio Networking and Communications)
• Rossano Gaeta, Marco Grangetto, Matteo Sereno, Local Access to Sparse and Large Global Information in P2P Networks: a Case for Compressive Sensing. (IEEE Int. Conf. on peer-to-peer computing, August 2010)
• Scott Pudlewski, Tommaso Melodia, Arvind Prasanna, C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks. (in Proc. of IEEE Intl. Conf. on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Boston, MA, June 2010)
• Lin, Y.G.; Zhang, B.C.; Hong, W.; Wu, Y.R.; , Along-track interferometric sar imaging based on distributed compressed sensing. (IET Electronics Letters, 46(12), pp. 858 - 860, June 2010 )
• Chong Luo, Feng Wu, Jun Sun, Chang Wen Chen, Compressive Data Gathering for Large-Scale Wireless Sensor Networks. (MobiCom '09 )
• Yasamin Mostofi and Alejandro Gonzalez-Ruiz, Compressive Cooperative Obstacle Mapping in Mobile Networks. (Proceedings of the 29th Military Communications Conference (Milcom), pp. 947-953, November 2010) [ACC 2009]
• J. M. Kim, O. K. Lee and J. C. Ye, Compressive MUSIC: A Missing Link between Compressive Sensing and Array Signal Processing. (IEEE Trans. on Information Theory, 2011 (in press))
• G. Oliveri and A. Massa, Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays. (IEEE Transactions on Antennas and Propagation, vol. 59, no. 2, pp. 467-481, Feb. 2011)
• Mu Lin, Chong Luo, Feng Liu and Feng Wu, Compressive Data Persistence in Large-Scale Wireless Sensor Networks. (IEEE Globecom, 2010)
• J. Oliver and Heung-No Lee, A Realistic Distributed Compressive Sensing Framework for Multiple Wireless Sensor Networks. (Signal Processing with Adaptive Sparse Structured Representation, Edinburgh, Scotland, June 27-30, 2011)
• Giulio Coluccia, Enrico Magli, Aline Roumy, Velotiaray Toto-Zarasoa, Lossy Compression of Distributed Sparse Sources: a Practical Scheme. (The 2011 European Signal Processing Conference (EUSIPCOâ��2011), 29/08/2011, Barcellona (Spain))
• Justin Ziniel and Philip Schniter, Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem. (Preprint, Nov. 2011) [Related conference publication: Asilomar 2011]

Model-based Compressive Sensing

• Marco Duarte, Fast reconstruction from random incoherent projections. (Rice ECE Department Technical Report TREE 0507, May 2005)
• 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)
• Marco Duarte, Michael Wakin, and Richard Baraniuk, Wavelet-domain compressive signal reconstruction using a hidden Markov tree model. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Yonina C. Eldar and Moshe Mishali, Robust Recovery of Signals From a Structured Union of Subspaces. (IEEE Trans. Inform. Theory, vol. 55, no. 11, pp. 5302-5316, November 2009)
• Richard Baraniuk, Volkan Cevher, Marco Duarte, and Chinmay Hegde, Model-based compressive sensing. (Preprint, 2008)
• Volkan Cevher, Marco Duarte, Chinmay Hegde, and Richard Baraniuk, Sparse signal recovery using Markov random fields. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2008)
• Ali Cafer Gurbuz, James H. McClellan, Justin Romberg, and Waymond R. Scott, Jr., Compressive sensing of parameterized shapes in images. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
• Marco F. Duarte, Chinmay Hegde, Volkan Cevher and Richard G. Baraniuk, Recovery of Compressible Signals in Unions of Subspaces. (Conference on Information Sciences and Systems (CISS), March 2009)
• Chinmay Hegde, Marco F. Duarte and Volkan Cevher, Compressive Sensing Recovery of Spike Trains Using a Structured Sparsity Model. (Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), April 2009.)
• Y.C. Eldar, P. Kuppinger, H. Bolcskei, Compressed Sensing of Block-Sparse Signals: Uncertainty Relations and Efficient Recovery. (Submitted to IEEE Transactions on Signal Processing, June 2009)
• Marco F. Duarte, Volkan Cevher and Richard G. Baraniuk, Model-Based Compressive Sensing for Signal Ensembles. (Allerton Conference on Communication, Control, and Computing, October 2009.)
• Chinmay Hegde and Richard G. Baraniuk, Compressive Sensing of Streams of Pulses. (Allerton Conference on Communication, Control, and Computing, October 2009.)
• Marco F. Duarte, Richard G. Baraniuk, Spectral Compressive Sensing. (Preprint, 2010)
• Chinmay Hegde and Richard G. Baraniuk, Compressive Sensing of a Superposition of Pulses. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas, March 2010)
• Chinmay Hegde and Richard G. Baraniuk, Sampling and Recovery of Pulse Streams. (Preprint, 2010)
• Phil Schniter, Turbo Reconstruction of Structured S