Compressive Sensing Listing /
An incomplete summary of the recent papers / links / blog posts listed on Nuit Blanche on the subject of compressive sensing or compressed sensing.
[The Compressive Sensing Blog][Compressive Sensing: The Big picture]
[Compressive Sensing 2.0 Community] [Compressive Sensing 2.0]
 NearOptimal Bayesian Localization via Incoherence and Sparsity byVolkan Cevher, Petros Boufounos, Richard Baraniuk, Anna Gilbert, Martin Strauss.
 Mario Figueiredo kindly released the SparSA code featured in “Sparse reconstruction by separable approximation” (new version) by Stephen Wright, Robert Nowak, Mario Figueiredo. The MATLAB code available here.
http://www.lx.it.pt/~mtf/SpaRSA  Lawrence Carin, On the Relationship Between Compressive Sensing and Random Sensor Arrays.
 A Simple Method for Sparse Signal Recovery from Noisy Observation Using Kalman Filtering by Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
 A Simple Method for Sparse Signal Recovery form Noisy Observations Using Kalman Filtering. Embedding Approximate QuasiNorms for Improved Accuracy also by Avishy Carmi, Pini Gurfil, Dimitri Kanevsky.
 Unscented Kalman Filter
 Postsilicon Timing Characterization by Compressed Sensing by Davood Shamsi, Petros Boufounos, Farinaz Koushanfar.
 Srikanth Jagabathula , Inferring Rankings Under Constrained Sensing.
 Accelerating SENSE Using Compressed Sensing (also viewable here) by Dong Liang, Bo Liu, JiunJie Wang, Leslie Ying.
 Regularized SENSE Reconstruction Using Bregman Iterations by Bo Liu, Kevin King, Michael Steckner, Jun Xie, Jinhua Sheng and Leslie Ying.
 SparseSENSE: RandomlySampled Parallel Imaging using Compressed Sensing by Bo Liu, Florian Sebert, Yi Ming Zou, and Leslie Ying.
 Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction by Xiaoqun Zhang, Martin Burger, Xavier Bresson, and Stanley Osher.
 Exploiting structure in waveletbased bayesian compressed sensing by Lihan He and Lawrence Carin. There is now an implementation of the TSWCS algorithm available as well as another implementation of the Bayesian Compressive Sensing package at Duke.
 A Coordinate Gradient Descent Method for l_1regularized Convex Minimization by Sangwoon Yun and KimChuan Toh.
 Yin Zhang is at it again, he just released two reconstruction codes with attendant papers. First a new code: RecPF and FTVd (version 3.0)
 a course on Connexions entitled A Class of Fast Algorithms for Total Variation Image Restoration.
 Michael Lustig just
pointed my attention to the work he and his coauthors were presenting
in th upcoming workshop on data sampling and image reconstruction.
There is 1 page abstract online and a related presentation on the subject entitled:
 Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P., SENSE: Sensitivity Encoding for Fast MRI , Magn Reson Med. 1999 Nov;42(5):95262.
Pruessmann KP, Weiger M, Börnert P, Boesiger P., Advances in sensitivity encoding with arbitrary kspace trajectories. Magn Reson Med. 2001 Oct;46(4):63851.  Mark Iwen about whether he would make his FFT algorithm available. The Ann Arbor Fast Fourier Transform available for download at sourceforge. This is the code implemented in Empirical Evaluation of a SubLinear Time Sparse DFT Algorithm.
 A Theoretical Analysis of Joint Manifolds by Mark Davenport, Chinmay Hegde, Marco Duarte, and Richard Baraniuk.
 Robert Calderbank, Stephen Howard and Sina Jafarpour entitled Construction of a Large Class of Deterministic Sensing Matrices that Satisfy a Statistical Isometry Property
 Image reconstruction by deterministic compressive sensing by Kangyu Ni, Somantika Datta, Svetlana Roudenko, Douglas Cochran.
 Compressed Signal Reconstruction using the Correntropy Induced Metric by Sohan Seth, Jose C. Principe. Now Sohan Seth also released the attendant poster and the code
 Compressed sensing imaging techniques for radio interferometry by Yves Wiaux, Laurent Jacques, Gilles Puy, Anna Scaife and Pierre Vandergheynst.
 Damien Segransan in Observability and UV coverage
 JeanLuc Starck just released his nice talk presented at the conference in honor of Jean Morlet entitled "Wavelet in Astronomy: From the Isotropic Undecimated WT to Compressed Sensing". I liked the slide updating us on the Compressive Sensing algorithm being currently implemented on the PACS instrument on Herschel due to be launched this Spring
 Dror Baron just let me know of the release of a new paper by him, Shriram Sarvotham and Rich Baraniuk entitled Bayesian Compressive Sensing via Belief Propagation. The attendant Matlab code implementing the CSBP algorithm
 Alexis Flesch (a Ph.D. student working for JeanYves Dauxois ) has produced a Python implementation of the Alternating L_1 method. The implementation uses cvxopt,
 Anna Gilbert, in her presentation entitled "Applications of Compressed Sensing to Biology", shows some of most recent results obtained with colleagues (one is Raghu Kainkaryam mentioned previously) performing Group Testing using Compressed Sensing ideas. The idea is to reduce the number of chips (SNP) for testing. The results are currently so so. The video is here.
 Stefano Marchesini points out that the following reference: Increasing FTIR spectromicroscopy speed and resolution through compressive imaging by Julien Gallet, Michael Riley, Zhao Hao and Michael C. Martin.
 Roummel Marcia and Rebecca Willett in Compressive Coded Aperture Superresolution Image Reconstruction ( the slides are here). I am also told by Ori Katz, one of the folks at Weizman performing Ghost Imaging (as mentioned here) that their reconstruction scheme is also linear. This is also the case of the coded aperture in the IR range that could be implemented in the LACOSTE program (as can be seen here and here).
 Andrew Ng presents his work in this video.
 Center for Computational Intractability has just released some of the videos of the Geometry and Algorithm workshop.
 Stephen Smale, Understanding Patterns in Data hires video lowres video
 Santosh Vempala, Isotropic Principal Components hires video lowres video
 James Lee, On the geometry of graphs and L_1 embeddings hires video lowres video
 Joel Tropp, Column subset selection, matrix factorization, and eigenvalue optimization hires video lowres video
 Assaf Naor, The Myth of Metric Dimension Reduction hires video lowres video
 Yury Makarychev, Lower bounds for Sherali Adams via localglobal metrics hires videolowres video
 Robi Krauthgamer, Metric Embeddings As Computational Primitives hires video lowres video
 Guy Kindler, Can cubic tiles be spherelike? hires video lowres video
 Leonard Schulman, Contraction and Expansion of Convex Sets hires video lowres video
 Venkat Guruswami, Explicit Euclidean sections from expander codes hires video lowres video
 Ben Recht,* Exact Lowrank Matrix Completion via Convex Optimization hires video lowres video
 Partha Niyogi, Manifold Learning: A geometric perspective on learning theory and algorithms hires video lowres video
 Sanjoy Dasgupta, Open problems in learning low dimensional representations hires video lowres video
 Anna Gilbert,* Applications of Compressed Sensing to Biology hires video lowres video
 Rump session: open problems, brief announcements, etc, Hires:* Akavia Alon Arora Khot Magen Naor, Lowres:8 Akavia Alon Arora Khot Magen Naor
 Yuval Rabani, Explicit construction of a small epsilonnet for linear threshold functions hires video lowres video
 Hamed Hatami, Graph norms and Sidorenko’s conjecture hires video lowres video
 Navin Goyal, Learning Convex Bodies is Hard (short talk) hires video lowres video
 Gideon Schechtman, Local Versions of Dimension Reduction hires video lowres video
 Ilan Newman, Online embedding of metrics hires video lowres video
 Prasad Raghavendra, A Simple SDP Gap Instance for Unique Games hires video lowres video
 Luis Rademacher, Expanders via Random Spanning Trees hires video lowres video
 Moritz Hardt, Rounding Parallel Repetitions of Unique Games hires video lowres video
 Alexandr Andoni, Hardness of Nearest Neighbor under L_infinity hires video lowres video
 Dror Baron has given some context to his Compressed Sensing research
 Adi Akavia made a presentation on the SFT algorithm that can "Deterministically and Locally Finding Significant Fourier Transform Coefficients". The video of the talk is here.
 Sanjeev Arora and Moses Charikar entitled “Semidefinite programming and approximation algorithms: a survey.” (video_part_1 and video_part_2) and the tutorial by Assaf Naor on “An Introduction to Embedding Theory”(video part 1 video part 2).
 Ramesh Raskar at ECTV'08. If you haven't had time to read his papers yet, here is a good way to get interested by watching the video.
 Group Testing in the Life Sciences
 Nicolas ThierryMieg is the author of the STD algorithm that was mentioned in the Shifted Transversal Designs and Expander Graphs entry of Raghu Kainkaryam's blog.
 Ghost imaging with a single detector by Yaron Bromberg, Ori Katz and Yaron Silberberg.
 Bayesian Pursuit Algorithm for Sparse Representation by Hadi Zayyani, Massoud BabaieZadeh and Christian Jutten.
 Piotr Indyk and Milan Ruzic , NearOptimal Sparse Recovery in the L1 norm featuring some of the results of the Sparse Recovery Experiments with Sparse Matrices.
 Group Testing and Sparse Signal Recovery by Anna Gilbert, Mark Iwen and Martin Strauss.
 The 12Balls Problem as an Illustration of the Application of Information Theory, Robert H. Thouless,
 Justin Haldar, Diego Hernando, and ZhiPei Liang, Compressed sensing in MRI with nonFourier encoding.
 NIPS '08 online papers
 Rachel Ward renamed "Cross validation in compressed sensing via the JohnsonLindenstrauss Lemma" into Compressed sensing with cross validation.
 Stephane Mallat made a presentation at ETCV'08
 Let It Wave devised technologies around the bandelet families , latest development of the this startup has been the conversion of today's videos into High Definition video. Presentation by Stephane Mallat entitled Sparse Geometric Superresolution,
 Using sparse representations for missing data imputation in noise robust speech recognition by Jort Gemmeke and Bert Cranen
 Larry Hornbeck , DMD technology in DLP projectors
 photon detector made out of Silicon
 Greedy Signal Recovery Review by Deanna Needell, Joel Tropp andRoman Vershynin.
 Moshe Mishali, Yonina Eldar and Joel Tropp just released Efficient Sampling and Stable Reconstruction of Wide Band Sparse Analog Signals
 http://perception.csl.uiuc.edu/coding/motion/.
 Pseudospectral Fourier reconstruction with IPRM by Karlheinz Gröchenig and Tomasz Hrycak.
 A proximal method for composite minimization, A.S. Lewis, S.J. Wright
 Comparing Measures of Sparsity, Niall P. Hurley, Scott T. Rickard
 Approximate Sparse Decomposition Based on Smoothed L0Norm Hamed Firouzi, Masoud Farivar, Massoud BabaieZadeh, Christian Jutten
 NoiseResilient Group Testing: Limitations and Constructions, Mahdi Cheraghchi
 Robust Regression and Lasso, Huan Xu, Constantine Caramanis, Shie Mannor
 Learning to rank with combinatorial Hodge theory, Xiaoye Jiang, LekHeng Lim, Yuan Yao, Yinyu Ye
 Toward 0norm Reconstruction, and a Nullspace Technique for Compressive Sampling as presented by Christine Law with Gary Glover
 Compressed Sensing Phase Retrieval with Matthew Moravec, Justin Romberg andRichard Baraniuk
 Ognyan Kounchev , A generalization of Kolmogorov’s theory of nwidths for infinite dimensional spaces: applications to Compressive sensing.
 new job posting for a postdoc in the group of JeanLuc Starck at CEA near Paris
 http://igorcarron.googlepages.com/cstechnologywatch
 GPUCamp
 Emerging Trends in Visual Computing meeting The official website of the workshop is here. The videos of the talks are listed below:
 Opening of ETVC'08 by JeanMarc Steyaert, Frank Nielsen
 Detection of Symmetries and Repeated Patterns in 3D Point Cloud Data by Leonidas Guiba
 Discrete Curvature Flow for Surfaces and 3Manifolds by Xiaotian Yin
 Certified Mesh Generation by JeanDaniel Boissonnat
 InformationTheoretic Algorithms for Diffusion Tensor Imaging by Baba C. Vemuri
 Statistical Computing on Manifolds for Computational Anatomy by Xavier Pennec
 LargeScale Object Recognition Systems by Cordelia Schmid
 Recovering Shape and Motion from Video Sequences by Pascal Fua
 Computational Geometry from the Viewpoint of Affine Differential Geometry by Matsuzoe Hiroshi
 Unifying Subspace and Distance Metric Learning with Bhattacharyya Coefficient for Image Classification by Dimitris N. Metaxas
 Nonstandard Geometries and Data Analysis by Suresh Venkatasubramanian
 Information Geometry and Its Applications by Shunichi Amari
 Information Geometry: Duality, Convexity and Divergences by Jun Zhang
 Computational Photography: Epsilon to Coded Imaging by Ramesh Raskar
 The Intrinsic Geometries of Learning by Richard Nock
 Applications of Information Geometry to Radar Signal Processing by Frédéric Barbaresco ( I have mentioned him before here)
 ConstantWorkingSpace Algorithms for Image Processing by Tetsuo Asano
 Sparse Geometric SuperResolution by Stephane Mallat
 Sparse Sampling: Variations on a Theme by Shannon by Martin Vetterli
 Image Retrieval via Kullback Divergence of Patches of Wavelets Coefficients in the kNN Framework by Michel Barlaud
 Machine learning and kernel methods for computer vision by Francis Bach
 3D Visibility and Lines in Space by Sylvain Lazard
 Procedural Modeling of Architectures: Towards Large Scale Visual Reconstruction by Nikos Paragios
 3D Video: A Fusion of Graphics and Vision by Markus Gross
 Opening of ETVC'08 by JeanMarc Steyaert, Frank Nielsen
 Terry Tao is in Spain and posted his presentation on The uniform uncertainty principle and compressed sensing.
 Michael Grant and Stephen Boyd released Version 1.2 of CVX: Matlab Software for Disciplined Convex Programming.
 Stefano Marchesini has just put up a presentation shown at the UTK Colloquium.
 Thong Do just released a large scale version of the Sparsity Adaptive Matching Pursuit package. More information can be found here.
 Ramesh Raskar has updated his Camera Culture site. One can read more about his fascinating hardware development projects here. He is also recruiting.
 Compressed Sensing with Sequential Observations by Dmitry Malioutov, Sujay Sanghavi, Alan Willsky.
 Instance Optimal Decoding by Thresholding in Compressed Sensing by Albert Cohen, Wolfgang Dahmen, and Ronald DeVore.
 L1Packv2: A Mathematica package for minimizing an $\ell_1$penalized functional by Ignace Loris.
 Streaming Measurements in Compressive Sensing: l_1 Filtering by M. Salman Asif and Justin Romberg.
 Salman Asif's thesis we mentioned earlier entitled: Primal Dual Pursuit: A homotopy based algorithm for the Dantzig selector. One can also read the presentation slides, errata listand attendant Matlab files.
 BlockSparsity: Coherence and Efficient Recovery by Yonina Eldar and Helmut Bolcskei
 Matthias Seeger has a new talk entitled Bayesian Optimization of MRI Sequences.
 Internships offered by Thales on the subject of Compressed Sensing. The announcements are here and here
 Piotr Indyk just released his Tutorial on Compressed Sensing (or Compressive Sampling or Linear Sketching)
 Paul O’Grady and Scott Rickard, Compressive Sampling of NonNegative Signals. The associated talk is here. http://ee.ucd.ie/~pogrady/NUIRLS/
 IMA Workshop in England is near Oxford
 Compressive Sensing Workshop
 Agence Nationale de la Recherche just came out with a new RFP entitled DOMAINES ÉMERGENTS ET PROGRAMME PHARE «MEMOIRE»
 Christine Law and Gary Glover entitled Toward 0norm Reconstruction, and a Nullspace Technique for Compressive Sampling.
 Anna Gilbert's talk entitled Combining Geometry and Combinatorics: A Unified Approach to Sparse Signal Recovery
 Prototyping Counter Braids on NetFPGA by Jianying Luo, Yi Lu and Balaji Prabhakar.
 Midwest Theory Day, Dec. 6, 2008, 10am5:00pm, Fine Barr Forum, Allen Center, Northwestern U., Evanston IL.Guangwu Xu
 Maryam Fazel, University of Washington, Parsimonious Models and Matrix Rank Minimization, December 2 , 2008, 10:30am  11:20am, Room 125 EEB
 Laurent Jacques is looking for some input on how to catch a Lion with compressed sensing
 Aleks Jakulin alerts us to the fact ( inNetflix Prize scoring function isn't Bayesian
 Shiqian Ma, Donald Goldfarb, Lifeng Chen, Fixed point and Bregman iterative methods for matrix rank minimization.
 Lee Potter, Phil Schniter, and Justin Ziniel just released A Fast Posterior Update for Sparse Undetermined Linear Models.
 Terry Tao will give a lecture in Norway entitled "Compressed sensing", inAuditorium EL2, Electrical Engineering Building , NTNU Gløshaugen
 Applied Mathematics Colloquium at Caltech, Monday December 1, 2008, 4:00 PM, 101 Guggenheim Lab, LeesKubota Lecture Hall, Title: New, Fast and Effective Algorithms for Imaging, Compressed Sensing and Related Problems, with Applications by Stanley Osher,
 Anatoli Juditsky and Arkadii Nemirovski have just released the code associated with their paper entitled: On Verifiable Sufficient Conditions for Sparse Signal Recovery via l_1 Minimization. http://sites.google.com/site/testsensingmatrices/
 Frank Nielsen and Ramesh Raskar
 The Design of Compressive Sensing Filterby Lianlin Li, Wenji Zhang, Yin Xiang, Fang Li.
 GPUCamp in Paris on December 6th.
 Matthias Seeger has an opening for a PhD Student / Postdoctoral Researcher in Machine Learning, Image Processing
 Exact Feature Extraction using Finite Rate of Innovation Principles with an Application to Image Superresolution by Loic Baboulaz and Pier Luigi Dragotti.
 GeometryDriven Distributed Compression of the Plenoptic Function: Performance Bounds and Constructive Algorithms by Nicolas Gehrig and Pier Luigi Dragotti.
 Andres CorradaEmmanuel entitled Guaranteeing Accuracy and Precision
 Hondani on Manifold Based Signal Recovery
 Counter Braids: Asymptotic Optimality of the Message Passing Decoding Algorithm by Yi Lu, Andrea Montanari and Balaji Prabhakar.
 An
introduction to compressive sensing, Seminar at University of
California, Berkeley, November 26 at 11:10 a.m.12 p.m., 380 Soda
Hall. Speaker/Performer: Prof. Olga Holtz, UC Berkeley Math,
 Counter Braids: A novel counter architecture for network measurement by Yi Lu
 Thomas Blumensath, http://www.see.ed.ac.uk/~tblumens/DSP2009_CS_SpecialSessionCFP.pdf, http://www.dsp2009.org/
 Real Time Elimination of Undersampling Artifacts in CE MRA using Variational Denoising on Graphics Hardware by Florian Knoll, Markus Unger, Franz Ebner, Rudolf Stollberger. The paper is here and the associated video is here.
 Sparse and stable Markowitz portfolios, by Joshua Brodie, Ingrid Daubechies, Christine De Mol, Domenico Giannone and Ignace Loris,
 Estimation of channelized features in geological media using sparsity constraint by Behnam Jafarpour.
 Lightweight Acoustic Classification for CaneToad Monitoring by Thanh Dang and Nirupama Bulusu and Wen Hu.
 Rachel
Ward, Title:Cross Validation in Compressed Sensing, When/where: Yale
University, Applied Math, Tuesday, November 25, 4:15PM, AKW 200
 Jean Francois Mercier, at INRIA Rocquencourt, France. November 27, 15:3016:30
 Jared tanner, OxfordMan Institute (9 Alfred Street, Oxford, UK, Mon, 17/11 at 15:45.
 Compressed sensing reconstruction of a string signal from interferometric observations of the cosmic microwave background, Yves Wiaux (EPFL, Lausanne, Switzerland), Tuesday 16 December 2008, 16:3017:30, Ryle Seminar Room, Cavendish Laboratory. University of Cambridge, UK
 Compressive Sensing: The Big Picture
 Compressive Sensing Hardware
 Compressed Sensing Videos
 Compressive Sensing Calendar
 Compressive Sensing Jobs
 Local Compressed Sensing Codes
 CS LinkedIn Group
 Recent links on the Blog in CS
 Compressive Sensing 2.0: blogs and webpages
 Compressive Sensing 2.0 Community
 Saturday Morning Cartoons
 Local Compressed Sensing Codes
 Moshe Mishali, Yonina Eldar and Joel Troppjust released Efficient Sampling of Sparse Wideband Analog Signals.
 IMA just hosted a Hot Topics Workshop entitled MultiManifold Data Modeling and Applications organized by Ron DeVore, Tamara Kolda, Gilad Lerman, and Guillermo Sapiro.
 Richard Baraniuk: Manifold models for signal acquisition, compression, and processing, Slides (pdf), Video (flv)
 Partha Niyogi : A Geometric perspective on machine Learning , Video (flv)
 Ronald DeVore: Recovering sparsity in high dimensions, Abstract (pdf), Video (flv)
 Ronald DeVore: Recovering sparsity in high dimensions, Abstract (pdf), Video (flv)
 Yi Ma : Dense error correction via L1 minimization, Video (flv)
 Ingrid Daubechies : Mathematical problems suggested by AnalogtoDigital conversion, Video (flv)
 Mauro Maggioni : Harmonic and multiscale analysis on lowdimensional data sets in highdimensions , Video (flv)
 Mark Davenport: Joint manifold models for collaborative inference, Poster (pdf)
 Julien Mairal: Supervised dictionary learning, Poster (pdf)
 Chinmay Hegde : Random projections for manifold learning , Poster (pdf)
 Ehsan Elhamifar : 3D motion segmentation via robust subspace separation, Poster (pdf)
 Saturday Morning Cartoon, Compressive Sensing Hardware
 The Design of Sparse Antenna Array by Lianlin Li, Wenji zhang and Fang Li.
 Identification of Matrices having a Sparse Representation by Gotz E. Pfander, Holger Rauhut, Jared Tanner.
 Petros Boufounos, "Compressive Sensing and Beyond: New approaches to signal acquisition and processing.", "L1 Minimization Without Amplitude Information." , "1Bit Compressive Sensing." , "Reconstructing Sparse Signals From their Zero Crossings."
 Stefano Marchesini , Large scale inverse problems in ultrafast xray imaging, A unified evaluation of iterative projection algorithms for phase retrieval,
 Ab initio compressive phase retrieval by Stefano Marchesini.
 Stefano Marchesini's presentation page, one can find additional material such as a list of most experiments that could use CS here. There are also two other talks (here and here).
 Theoretical Analysis of Compressive Sensing via Random Filter by Lianlin Li, Yin Xiang and Fang Li.
 Fast GPU Implementation of Sparse Signal Recovery from Random Projections by Mircea Andrecut
 Richard Baraniuk showing his latest introduction to Compressed Sensing in a video.
 In Defense of NearestNeighbor Based Image Classification by Oren Boiman, Eli Shechtman and Michal Irani.
 Identification of Matrices having a Sparse Representation by Gotz Pfander, Holger Rauhut and Jared Tanner.
 The Compressive Structured Light for Recovering Inhomogeneous Participating Media is here.
 Sequential adaptive compressed sampling via Huffman codes by Akram Aldroubi, Haichao Wang, Kourosh Zarringhalam.
 Daniel Rouan before devising nulling interferometers ProuhetTarryEscott [2] (PTE) , Diophantine Optics explanation.
 Diophatine Optics, Daniel Rouan
 [2] Weisstein, Eric W. "ProuhetTarryEscott Problem." From MathWorldA Wolfram Web Resource.http://mathworld.wolfram.com/ProuhetTarryEscottProblem.html
 Guest editor's comments on special issue on nonuniform sampling by Farokh Marvasti.
 One Video Stream to Serve Diverse Receivers by Szymon Chachulski, Dina Katabi and Grace Woo.
 Postdoctoral fellow in Sensor Networks & Compressive Sensing at the Institute of Computer Science (ICS), Foundation for Research and TechnologyHellas (FORTH) in Heraklion, Crete, Greece.
 Análisis y reconocimiento de patrones en electroforesis capilar utilizando compressed sensing by Alvaro Hernández Orence, Hernández Orence, Alvaro, Paredes Quintero, José Luis at Universidad de Los Andes in Venezuela.
 Valerio Cambareri's thesis is here (it is in Italian).
 wiki: Practical nearoptimal sparse recovery in the ell1 norm by Radu Berinde, Piotr Indyk and Milan Ruzic.
 Exploiting structure in waveletbased bayesian compressed sensing by Lihan He and Lawrence Carin.
 Hashbased identification of sparse image tampering by Giorgio Prandi, Giuseppe Valenzise, Marco Tagliasacchi, Augusto Sarti [See also related conference publication: ICIP 2008].
 Identification of sparse audio tampering using distributed source coding and compressive sensing techniques by Giorgio Prandi, Giuseppe Valenzise, Marco Tagliasacchi, Augusto Sarti [See also related conference publication: DAFX 2008]
 Efficient Seismic Forward Modeling using Simultaneous Random Sources and Sparsity by Ramesh Neelamani, C. Krohn, J. Krebs, M. Deffenbaugh, J. E. Anderson and Justin Romberg.
 Stephane Chretien just made available his code for the Alternating l1 Method for Compressed Sensing that we mentioned earlier. The page for the code is here.
 Venkatesan Guruswami gave a talk entitled:Expander codes, explicit Euclidean sections, and compressed sensing,
 The Center for Computational Intractability will organize a Workshop on Geometry and Algorithms on October 2931 at the Friend Center 06 at Princeton.
 Adi Akavia, recently presented a talk at DIMACS entitled: Locally & Universally Finding Significant Fourier Coefficients.
 Pierre Weiss defended his dissertation on October 21.
 Saturday Morning Cartoon Series : http://igorcarron.googlepages.com/saturdaymorningcartoons
 Gabriel Peyre, perform decomposition of textures. Bob Plemmons hyperspectral detection of space junk (explained here).
 Nature article of Daniel Lee and Sebastian Seung entitled Learning the parts of objects by nonnegative matrix factorization)
 NonNegative Matrix Factorization, Convexity and Isometry byNikolaos Vasiloglou, Alexander Gray, David Anderson
 Gabriel Peyre, Sebastien Bougleux,Laurent Cohen entitled Nonlocal Regularization of Inverse Problems.
 Valerio Cambareri his presentation available in pdf and ppt format. Conversione AnalogToInformation Tramite Architettura Random Modulation Preintegration
 Laurent Duval : Emerging Trends in Visual Computing (ETVC'08) and will take place on November 18th20th. It is in the calendar. Registration is free until the November 1st, 2008.
 The SLIM blog has a summary entry on Compressed sensing versus sparse recovery
 Jacket: a GPU engine for Matlab. You may want to download the beta version before it becomes a paid product.
 Ivana Jovanovic at EPFL just released her Ph.D thesis entitled Inverse problems in acoustic tomography.
 Jainfeng Cai, Emmanuel Candès and Zuowei Shen : A singular thresholding algorithm for matrix completion.(One can also find it also here, here, or here on ArXiv)
 http://igorcarron.googlepages.com/compressedsensinghardware
 Piotr Indyk and Radu Berinde just made live a wiki on Sparse Recovery Experiments
Gabriel Peyre released a set of matlab experiments to illustrate the 3rd edition of the "A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way" book of Stephane Mallat, which will feature chapters about sparse representations and compressive sensing: http://www.ceremade.dauphine.fr/~peyre/wavelettour/
 Terry Tao, Small samples, and the margin of error.
 Venkatesan Guruswami,James R. Lee, and Avi Wigderson : Euclidean sections of l^N_1 with sublinear randomness and errorcorrection over the reals.
 SISYPH (Signals, Systems and Physics) research group, at the Physics Department of Ecole Normale Superieure de Lyon. More information can be found here.
 Yves Lucet just released the Computation Convex Analysis Library.
 JeanFrancois Berger 'Possible rationales for RenyiTsallis entropy maximization'.
 Thomas Blumensath and Mike Davies : A Simple, Efficient and Near Optimal Algorithm for Compressed Sensing .
 Yuanqing Lin: l(1)norm sparse Bayesian learning: Theory and applications
 François Malgouyres and Tieyong Zeng: A Predual Proximal Point Algorithm solving a Non Negative Basis Pursuit Denoising model.
 CMOS Compressed Imaging by Random Convolution (also here) by Laurent Jacques, Pierre Vandergheynst, Alexandre Bibet, Vahid Majidzadeh, Alexandre Schmid, Yusuf Leblebici.
 Compressive Sampling of Pulse Trains : Spread the Spectrum ! (also here) by Farid Naini, Rémi Gribonval,Laurent Jacques and Pierre Vandergheynst.
 Lee Potter, Phil Schniter, and Justin Ziniel Fast Bayesian Marching Pursuit
 Emmanuel Candes: tutorial on Compressed Sensing.
 Fast and Efficient Dimensionality Reduction Using Structurally Random Matrices by Thong Do, Lu Gan , Yi Chen, Nam Nguyen and Trac Tran.
 Thong Do, Yi Chen, Nam Nguyen, Lu Gan and Trac Tran have also released A Fast and Efficient Heuristic Nuclear Norm Algorithm for Rank Minimization.
 SLIM lab: Unanswered questions in compressed sensing
 Edo Liberty : Accelerated Dense Random Projections. The slides of the talk are here.
 Justin Romberg Compressive sensing by random convolution
 Compressive Structured Light for Recovering Inhomogeneous Participating Media, by Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi
 Umar Syed, Robert Schapire, and Michael Bowling have just released Apprenticeship Learning Using Linear Programming. (web page). The video of the ICML presentation on the same subject is here.
 "Real" SlepianWolf Codes by Bikash Kumar Dey, Sidharth Jaggi, Michael Langberg.
 Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models by Matthias W. Seeger and Hannes Nickisch.
 Ping Li has released his latest paper on Compressed Counting. We mentioned a presentation on that subject and other work before.
 Graham Cormode did a presentation/tutorial on Data stream algorithms last July. It was a tutorial presented at the Bristol Summer School on Probabilistic Techniques in Computer Science. The video is here. The ppt slides are here while the pdf slides are here.
 Felix Herrmann, Yogi Erlangga and Tim Lin, Compressive simultaneous fullwaveform simulation.
 SampTA '09
 Martin Vetterli will give a talk at Columbia on October 22. The title of the talk is Sparse Sampling: Variations on a Theme by Shannon
 Yaron Rachlin and Dror Baron on The Secrecy of Compressive Sensing Measurements
 David Donoho and Jared Tanner in Counting faces of randomlyprojected polytopes when the projection radically lowers dimension.
 Volkan Cevher, Ali Gurbuz, James McClellan, and Rama Chellappa, Compressive wireless arrays for bearing estimation of sparse sources in angle domain
 Ali Gurbuz, James McClellan, and Volkan Cevher, A compressive beamforming method.
 Yasamin Mostofi and Pradeep Sen, Compressed mapping of communication signal strength.

 Jon Dattorro book on Convex Optimization: Convex Iteration( Chapter 4 page 306 to 314.). The Matlab code that realizes the algorithm (available here)
 Gradient based method for cone programming with application to largescale compressed sensing by Zhaosong Lu. The attendant Matlab source codes are here.
 Manifold Models for Signals and Images by Gabriel Peyre
 Thresholded Basis Pursuit:
Quantizing Linear Programming Solutions for Optimal Support Recovery
and Approximation in Compressed Sensing by Venkatesh Saligrama, Manqi Zhao
 A New Reconstruction Approach to Compressed Sensing by Tianjing Wang and Zhen Yang
 Michael Wakin just released on the ever fascinating subject of Manifold Signal processing in ManifoldBased Signal Recovery and Parameter Estimation from Compressive Measurements
 Detection and Identification of Sparse Audio Tampering Using Distributed Source Coding and Compressive Sensing Techniques by G. Prandi, Giuseppe Valenzise, Marco Tagliasacchi, Augusto Sarti.
 Shamgar Gurevich and Ronny Hadani, Incoherent dictionaries and the statistical restricted isometry property
 Jessica Nelson and Vladimir Temlyakov released On the size of incoherent systems
 Waheed Bajwa, Akbar M. Sayeed, and Robert Nowak also released Learning sparse doublyselective channels.
 Matthias Seeger produced a talk on Large Scale Approximate Inference and Experimental Design for Sparse Linear Models. The video of the talk can be found here. The attendant technical report has been released by Matthias Seeger and Hannes Nickisch. It is entitled:Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models.
 Yves Meyer's recent presentation entitled: Compressed sensing and transference (the paper is here "A variant of compressed sensing "), Islamic Art.
 Peter Lu , Quasicrystals in Medieval Islamic Architecture. The Science paper is here and the additional material is here. Paul Steinhardt the coauthor of the study has a webpage
 A computable Fourier condition generating aliasfree sampling lattices by Yue M. Lu, Minh Do and Richard Laugesen
 Duncan,J.L., Zhang,Y., Gandhi,J., Nakanishi,C., Othman,M., Branham,K.H., Swaroop,A., Austin Roorda High resolution imaging of foveal cones in patients with inherited retinal degenerations using adaptive optics, Invest.Ophthalmol.Vis.Sci. 48: 32833291 (2007)
17. Austin Roorda, A., Williams, D.R., The Arrangement of the Three Cone Classes in the Living Human Eye Nature 397, 520522 (1999).
Alexandre Borghi, Jerome Darbon, Sylvain Peyronnet, Tony F. Chan and Stanley Osher, A Simple Compressive Sensing Algorithm for Parallel ManyCore Architectures
 Three Novel Edge Detection Methods for Incomplete and Noisy Spectral Data by Eitan Tadmor, Jing Zou.
 Minimum Sum of Distances Estimator: Robustness and Stability by Yoav Sharon, John Wright, and Yi Ma
 Ali Cafer Gurbuz, Compressive sensing Ground penetrating radar Subsurface imaging
 Muhammad Salman Asif, Primal Dual Pursuit: A homotopy based algorithm for the Dantzig selector
 Supervised Dictionary Learning by Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman
 Yonina Eldar, Uncertainty Relations for Analog Signals
 CS calendar.
NIPS*2008 Workshop http://opt2008.kyb.tuebingen.mpg.de/
 Remi Gribonval , SPARS 2009.
 Stephane Mallat, A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way.
 Laurent Duval points me to Strobl09: Conference on TimeFrequency
 ISMRM Workshop on Data Sampling and Image Reconstruction: MRI Unbound.
 Dave Donoho
 INTERNET 2009
 IMMERSCOM 2009
 Valerio Cambareri, University of Bologna, , http://sites.google.com/site/vcambareri/ , Compressive Sampling via an RMPI
 In Wikimization, presentation by Christine Law
 G. Hosein Mohimani, Massoud BabaieZadeh and Christian Jutten entitled Fast Sparse Representation based on Smoothed l0 norm. A fast approach for overcomplete sparse decomposition based on smoothed L0 norm. Smoothed L0 (SL0) Algorithm for Sparse Decomposition
 Anatoli Juditsky and Arkadii Nemirovski, On Verifiable Sufficient Conditions for Sparse Signal Recovery via $\ell_1$ Minimization
 Graph Laplacian Tomography from Unknown Random Projections by Ronald Coifman,Yoel Shkolnisky, Fred Sigworth and Amit Singer.
 CryoEM Structure Determination through Eigenvectors of Sparse Matrices by Ronald Coifman,Yoel Shkolnisky, Fred Sigworth and Amit Singer.
 Dror Baron's talk
 Compressive Structured Light for Recovering Inhomogeneous Participating Mediaby Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi is presented very nicely in a video located here. It was added to the video section of the CS pages.
 Thomas Blumensath and Mike Davies have a revised version of "Sampling Theorems for Signals from the Union of Linear Subspaces".
 Vladimir Rokhlin, Arthur Szlam, and Mark Tygert, A Randomized Algorithm for Principal Component Analysis.
 Applied Math Seminar at the Computer Science Department at Yale, Shai Dekel, Adaptive compressed image sensing based on wavelettrees" and "On the equivalence of the modulus of smoothness and the Kfunctional over convex domains".
 Rafael Carrillo entitled: Robust Sampling and Reconstruction Methods for Sparse Signals in the Presence of Impulsive Noise
 Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients by Matthieu Kowalski and Bruno Torrésani.
 Mixed norms links
 Bruno Torrésani presentation in French entitled: Parcimonie, ondelettes et *lettes
 Eric Weinsteinentitled Sheldon Glashow Owes me a Dollar (and 17 years of interest): What happens in the marketplace of ideas when the endless frontier meets the efficient frontier?
 Lee Potter, Phil Schniter, and Justin Ziniel , Fast Bayesian Matching Pursuit. Fast Bayesian Matching Pursuit: Model Uncertainty and Parameter Estimation for Sparse Linear Models.
 Emmanuel Ravelli , two packages based on MPTK
Martin Vetterli, Sparse Sampling: Variations on a Theme by Shannon. The slides are here (12Mb).  Francis Bach , Learning with sparsity inducing norms
 Ping Li , Compressed Counting
 Separation of stereo speech signals based on a sparse dictionary algorithm by Maria Jafari and Mark Plumbley.
 Wikimization has a series of compressed sensing related papers and presentations here:
 Compressed Sensing with Contiguous Fourier Measurements: Presented by JeanFrançois Mercier with Laurent Demanet and George Papanicolaou at the Applied Mathematics Seminar, Stanford University, July 21, 2008
 Optimization Problems in Compressed Sensing: by Jalal Fadili, CNRS, ENSI Caen France, at the Applied Mathematics Seminar, Stanford University, July 21, 2008
 Compressed Sensing in Astronomy: Presented by JeanLuc Starck with Jérôme Bobin at the Applied Mathematics Seminar, Stanford University, July 21, 2008
Highly Undersampled 0Norm Reconstruction: Presented by Christine Law at Lucas Center for Imaging, Stanford University, July 9, 2008 (771KByte)
Advances in Compressive Sensing for MRI: Presented by Joshua Trzasko with Armando Manduca at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008
Nonconvex Compressive Sensing: Presented by Rick Chartrand with Valentina Staneva, Wotao Yin, & Kevin Vixie at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008
 Wikimization site, I found the following two sets of presentations:
 International Society for Magnetic Resonance in Medicine (ISMRM Toronto 2008), Randy Duensing & Feng Huang (requires Adobe Flash Player): Objective Comparison of Alternate Reconstruction Strategies: An Unmet Need. You need to go to the Wikimization site to find out both the username and password.
 Convex Optimization, Stanford University, Stephen Boyd: Tutorials for a graduate level course, 2008
 MultiTask Compressive Sensing with Dirichlet Process Priors by Yuting Qi, Dehong Liu, Lawrence Carin, David Dunson
 Necessary and Sufficient Conditions for Success of the Nuclear Norm Heuristic for Rank Minimization, Benjamin Recht, Weiyu Xu, and Babak Hassibi
 Sparse signal recovery using Markov random fields by Volkan Cevher, Marco Duarte, Chinmay Hegde, and Richard Baraniuk.
 Volkan Cevher
 GCOptimization package, Olga Veksler
 Compressive sensing for sensor calibration by Volkan Cevher, and Richard Baraniuk.
 Dikpal Reddy, Aswin Sankaranarayanan, Volkan Cevher, and Rama Chellappa, Compressed sensing for multiview tracking and 3D voxel reconstruction.
 Joel Tropp, Corrigendum in 'Just relax: Convex programming methods for identifying sparse signals in noise
 NearOptimal Sparse Recovery in the L1 norm, by Piotr Indyk and Milan Ruzic.
 Rayan Saab and Ozgur Yilmaz entitled Sparse recovery by nonconvex optimization  instance optimality.
 New Directions in Complex Data Analysis for Emerging Applications
 Fast disambiguation of superimposed images for increased field of view, Roummel F. Marcia, Changsoon Kim, Jungsang Kim, David Brady, and Rebecca Willett
 Compressive coded aperture video reconstruction, Roummel Marcia and Rebecca Willett. Videos are here.
 New Directions in Complex Data Analysis for Emerging Applications
 Jon Dattorro, Wikimization
 Laurent Jacques, SPGL1 and TV: Answers from SPGL1 Authors
 Mark Newman,The firstmover advantage in scientific publication
 Compressive Sensing Resources at Rice, this blog, Compressive Sensing 2.0 page and in the Compressive 2.0 Community page
 Laurent Demanet and Gabriel Peyré ,Compressive Wave Computation.
 Jarvis Haupt,Waheed Bajwa, Gil Raz and Robert Nowak ,Toeplitz compressed sensing matrices with applications to sparse channel estimation.
 David Brady, Optical Imaging and Spectroscopy
 My Amazon Wish List.
 http://compressedsensing.googlepages.com/
 Thomas Blumensath, Workshop on Sparsity and its application to large inverse problems
 Compressed Sensing and Bayesian Experimental Design by Mathias Seeger, Hannes Nickisch. The abstract of the presentation
 Autonomous Geometric Precision Error Estimation in Lowlevel Computer Vision Tasks, Andres CorradaEmmanuel and Howard Schultz. The abstract of the paper
 Davood Shamsi, Petros Boufounos and Farinaz Koushanfar in Noninvasive Leakage Power Tomography of Integrated Circuits by Compressive Sensing.
 Dense Error Correction via l1 Minimization, John Wright and Yi Ma
 Gabriel Peyre, Toolbox Sparsity  A toolbox for sparse coding and sparse regularization. The toolbox can be downloaded from Matlab Central.Multiwavelet Alpert Transform. Other Toolboxes can be found here.
 A Generalized Sampling Method for FiniteRateofInnovationSignal Reconstruction by Chandra Sekhar Seelamantula, Michael Unser.
 Compressed sensing based estimation of doubly selective channels using a sparsityoptimized basis expansion by Georg Tauböck and Franz Hlawatsch.
 On some deterministic dictionaries supporting sparsity by Shamgar Gurevich, Ronny Hadani, Nir Sochen.
 A Very Efficient Scheme for Estimating Entropy of Data Streams Using Compressed Counting by Ping Li.
 Lawrence Carin , Exploiting Structure in Statistical CompressiveSensing Inversion
 Videolectures.netHierarchical Kernel StickBreaking Process for MultiTask Image Analysis is there.
 L1based relaxations for sparsity recovery and graphical model selection in the highdimensional regime by Martin J. Wainwright.
 Roman Vershynin,
 Sparse representations and invertibility of random matrices. July 2007. AMS 2007 Von Neumann Symposium "Sparse Representation and HighDimensional Geometry", Snowbird, UT
 Compressed Sensing. September 2007. Applied Mathematics Seminar, UC Davis
 Randomness in functional analysis: towards universality. December 2007. Colloquium, Georgia Institute of Technology
 Small ball probability, additive structure and random matrices. March 2008, DIMACS. Rutgers. April 2008, UC Davis
 Call
for Papers, IEEE Signal Processing Society, IEEE Journal of Selected
Topics in Signal Processing: Special Issue on Compressive Sensing.
 Gabriel Peyre, Toolbox Sparsity  A toolbox for sparse coding and sparse regularization. The toolbox itself can be downloaded from Matlab Central.Multiwavelet Alpert Transform. Other Toolboxes can be found here.
 Performance limits for jointly sparse signals via graphical models by Marco Duarte, Shriram Sarvotham, Dror Baron, Michael Wakin , and Richard Baraniuk.
 Poster.
 Michael Lustig just released his Ph.D thesis entitled SPARSE MRI.
 Richard Baraniuk just made his "tutorial" presentation at EUSIPCO available. It is entitledTheory and applications of compressive sensing.
 Modelbased compressive sensing by Richard Baraniuk, Volkan Cevher, Marco Duarte, and Chinmay Hegde.
 Detailed explanation of Antonin Chambolle's algorithm for the resolution of compressed sensing with TV regularization.
 Gabriel Peyre, Literature Review on Sparse Optimization.
 Known examples or benchmarks (see end of list for images) as featured in the Sparco
 David Mary, A Comparison of the Reconstruction Capability of CoSaMP, OMP, Subspace Pursuit and Reweighted Lp
 Evgeniy Lebed with Felix Herrmann, Yogi Erlangga and Tim Lin,Interpolating solutions of the Helmhotz equation with compressed sensing
 Robust recovery of signals from a union of subspaces by Yonina Eldar and Moshe Mishali,
 Underdetermined source separation via mixednorm regularized minimization by M. Kowalski, E. Vincent and Remi Gribonval and,
 SparsityEnforced SliceSelective MRI RF Excitation Pulse Design by Adam Zelinski, Lawrence L. Wald, Kawin Setsompop, Vivek Goyal and Elfar Adalsteinsson
 Michael Friedlander, SPGL1,
 SPGL1 and TV minimization post byLaurent Jacques.
 SPARCOtechnical report.
 CVX ( Matlab Software for Disciplined Convex Programming ) by Michael Grant, Stephen Boyd and Yinyu Ye.
 Guaranteed MinimumRank Solutions of Linear Matrix Equations via Nuclear Norm Minimization by Benjamin Recht, Maryam Fazel, and Pablo Parrilo.
 Mikkel Schmidt, Bayesian nonnegative matrix factorization (NMF) using a Gibbs sampling approach.
 JianFeng Cai, Stanley Osher, and Zuowei Shen, Linearized Bregman Iterations For FrameBased Image Deblurring
 3D reconstruction from a single image by Diego Rother and Guillermo Sapiro.
 Gerry Skinner (CS: A Short Discussion with Gerry Skinner, a Specialist in Coded Aperture Imaging.)
 Roummel Marcia and Rebecca Willett (Compressive Coded Aperture Superresolution Image Reconstruction, the slides are here). Clarification available here.
 DARPA program, Part I (September 2007) and Part II (August 2008).
 Siggraph
 Programmable Aperture Photography: Multiplexed Light Field Acquisition, ChiaKai Liang, TaiHsu Lin, BingYi Wong, Chi Liu, Homer Chen.
 Martin Fuchs, Ramesh Raskar, HansPeter Seidel, and Hendrik P. A. Lensch entitled Towards Passive 6D Reflectance Field Displays.
 liquid lenses for webcams from Varioptic.
 Stan Osher, wiki.
 Ronald DeVore, Guergana Petrova, and Przemysław Wojtaszczyk, Instanceoptimality in probability with an ell1 decoder
 LinkedIn Group : Compressive Sensing Study Group.
 Amit Agrawal,Fall Internship (2008) at Mitsubishi Electric Research Labs (MERL)
 Recent reports of the Computational and Applied Mathematics department at UCLA
 Future of Science entry.
 Gabriel Peyre, Chambolle's algorithm for the resolution of compressed sensing with TV regularization.
 Antonin Chambolle, An Algorithm for Total Variation Minimization and Applications.
 Laurent Jacques, SPGL1 and TV minimization ?
 *lets
 A hitchhiker’s guide to the galaxy of transformdomain sparsification by Evgeniy Lebed and Felix Herrmann.
 From I am no geek blog, : Combining Compressed Sensing and Parallel Imaging by K. F. King.
 PhD studentship in London examining Compressive Sensing of audio scenes (added to CSjobs)
 Felix Herrmann, Yogi Erlangga and Tim Lin, Compressive sampling meets seismic imaging
 Compressive simultaneous fullwaveform simulation,, Felix Herrmann, Yogi Erlangga and Tim Lin.
 Threedimensional sparseaperture movingtarget imaging by Matthew Ferrara, Julie Jackson, Mark Stuff.
 Matthias Seeger, Large Scale Approximate Inference and Experimental Design for Sparse Linear Models. The video of the talk can be found here.
 Source code. Thomas Serre, Aude Oliva and Tomaso Poggio , "A feedforward architecture accounts for rapid categorization". The description and installation instructions are here. One can download the code here.
http://igorcarron.googlepages.com/csjobs.
Mathematics for Analysis of Petascale Data.
Raytheon is looking for an ATR Algorithm Design Engineer Gerry Skinner, Coded Mask Imagers: when to use them, and when not ?
NASA's Swift mission (http://heasarc.gsfc.nasa.gov/docs/swift/archive/) and ESA's Integral http://isdc.unige.ch/index.cgi?Data+info
Hadamard Transform Optics
 Association Schemes: Designed Experiments, Algebra and Combinatorics
By Rosemary Bailey  Mead R. The nonorthogonal design of experiments.
 Gerry's list of publications can be found here.
 Roummel Marcia and Rebecca Willett, Compressive Coded Aperture Superresolution Image Reconstruction (the slides are here).
 Multiplex imaging with random arrays, Christopher M. Brown, Ph.D. Thesis, Institute for Computer Research, University of Chicago, 1972.
 Analysis of data from codedmask telescopes by maximum likehood. Skinner, G. K., Nottingham, M. R., Nucl. Instrum. Methods Phys. Res., Sect. A, Vol. 333, No. 23, p. 540  547
 Two new methods for retrieving an image from noisy, incomplete data and comparison with the Cambridge MaxEnt package., Ustundag, D.; Queen, N. M.; Skinner, G. K.; Bowcock, J. E., International Workshop on Maximum Entropy and Bayesian Methods, MaxEnt 90, p. 295  301
 Reconstruction of images from a codedaperture box camera, Hammersley, Andrew; Ponman, Trevor; Skinner, Gerry, Nuclear Instruments and Methods in Physics Research Section A, Volume 311, Issue 3, p. 585594.
 Sensitivity of coded mask telescopes, Skinner, Gerald K., Applied Optics IP, vol. 47, Issue 15, pp.27392749
 Richard Baraniuk is looking for a postdoc in the area of compressive sensing, sparse signal and image processing
 The Split Bregman Method for L1 Regularized Problems by Tom Goldstein, Stan Osher was mentioned before. Matlab/mex code is here.
 Compressive Structured Light for Recovering Inhomogeneous Participating Media by Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi.
 Wei Dai, Mona Sheikh, Olgica Milenkovic, and Richard Baraniuk, Compressive sensing DNA microarrays.
 Volkan Cevher, Aswin Sankaranarayanan, Marco Duarte, Dikpal Reddy, Richard Baraniuk, and Rama Chellappa, Compressive sensing for background subtraction.
 Marco Duarte, Shriram Sarvotham, Dror Baron, Michael Wakin , and Richard Baraniuk, Performance limits for jointly sparse signals via graphical models. The technical report.
 Yonina Eldar and Moshe Mishali, Robust recovery of signals from a union of subspaces.
 Yin Zhang, On theory of compressive sensing via ell1minimization: Simple derivations and extensions.
 Jianwei Ma and FrancoisXavier Le Dimet, Deblurring from highly incomplete measurements for remote sensing.
 S. Dekel, Adaptive compressed image sensing based on wavelettrees.
 David Donoho and Jared Tanner, Counting the faces of radomlyprojected hypercubes and orthants, with applications.
 Justin Romberg, Compressive sensing by random convolution.
 Justin Romberg, Imaging via compressive sampling.
 Simon Foucart, MingJun Lai, Sparsest solutions of underdetermined linear systems via minimization for . The code is here.
 Testing the Nullspace Property using Semidefinite Programming by Alexandre d'Aspremont and Laurent El Ghaoui.
 "A short Introduction to Compressed Sensing" Emmanuel Candes, scivee.tv.
 Tensor Methods for Hyperspectral Data Processing: A Space Object Identification Study
Qiang Zhang, Han Wang, Robert J. Plemmons, and V. Paul Pauca  Phoenix landed on Mars, it was caught landing by the Mars Reconnaissance Orbiter's HiRISE camera (more here). Emily Lakdawalla has a detailed explanation on how this amazing shot was taken.
 Making a Modern 3D, Movie Journey to the Center of the Earth 3D shows off Hollywood's most advanced technology by Kate Greene who features the 3d PACE cameras
 Gravimetric Detection by Compressed Sensing by Marina Meila, Caren Marzban, Ulvi Yurtsever.
 Gianluca Monaci and Friedrich Sommer, Learning Sparse Representations for Audiovisual Signals.
 Reconstrução de imagens subamostradas (compressed sensing) by Mário Figueiredo.
 École d'Été annuelle en traitement du signal et des images in the village of Peyresq, Stéphane Chrétien, An Alternating l_1 Relaxation for compressed sensing.
 EUSIPCO, Theory and Applications of Compressive Sensing by Richard Baraniuk.
 Closing the Loop on Scene Interpretation by Derek Hoiem, Alexei A. Efros, Martial Hebert. Here is a 3D reconstruction video
 Inderjit Dhillon, Rank Minimization via Online Learning.
 Restricted isometry constants where lpminimization can fail for p above 0 and less or equal to 1,Remi Gribonval, Mike Davies.
 Presentation at Rice by Rachel Ward: Cross Validation in Compressed Sensing via the Johnson Lindenstrauss Lemma.
 Efficient Compressed Sensing using Lossless Expander Graphs with Fast Bilateral Quantum Recovery Algorithm, Sina Jafarpour
 MMDS 2008. Workshop on Algorithms for Modern Massive Data Sets. All the abstracts are located here. All the presentations are here.
 Piotr Indyk, Sparse recovery using sparse random matrices/ Or: Fast and Effective Linear Compression, Radu Berinde, Anna Gilbert, Piotr Indyk, Howard Karloff, Milan Ruzic and Martin Strauss
 Anna Gilbert, Combinatorial Group Testing in Signal Recovery.Radu Berinde, Piotr Indyk, Howard Karloff, Martin Strauss, Raghu Kainkaryam and Peter Woolf.
 Tong Zhang, An Adaptive Forward/Backward Greedy Algorithm for Learning Sparse Representations (the technical report is here: ForwardBackward Greedy Algorithm for Learning Sparse Representations)
 Nir Ailon, Edo Liberty, Efficient Dimension Reduction.
 Yoram Singer, Efficient Projection Algorithms for Learning Sparse Representations from High Dimensional Data.
 Kenneth Clarkson, Tighter Bounds for Random Projections of Manifolds (this is the report).
 Sanjoy Dasgupta, Yoav Freund, Random Projection Trees and Low Dimensional Manifolds.
 Lars Kai Hansen, Generalization in HighDimensional Matrix Factorization.
 Holly Jin, Michael Saunders, Exploring Sparse NonNegative Matrix Factorization.
 Compressed Counting and Stable Random Projections, Ping Li.
 Ronald Coifman, Diffusion Geometries and Harmonic Analysis on Data Sets.
 Thomas Blumensath, Mike Davies , Compressed Sensing and their attendant research in that field.
 Sami Kirolos, Tamer Ragheb, Jason Laska, Marco F. Duarte, Yehia Massoud and Richard Baraniuk entitled Practical Issues in Implementing AnalogtoInformation Converters.
 ESTC2008. The papers are here. Novel Distributed Wavelet Transforms and Routing Algorithms for Efficient Data Gathering in Sensor Webs. Antonio Ortega, G. Shen S. Lee S.W. Lee S. Pattem A. Tu B. Krishnamachari, M. Cheng S. Dolinar A. Kiely M. Klimesh, H. Xie
 Quelques Applications du Compressed Sensing en Astronomie, David Mary and Olivier Michel.
 Another presentation from the Rice group, Richard Baraniuk.
 Weiyu Xu, Babak Hassibi, Compressed sensing over the Grassmann manifold: A unified analytical framework.
 Honi Dan: 論文リスト（CS) in Japanese,
 Clifford Lindsay in his directed Research blog,
 Andriyan Suksmono:Ikebana@ITU.Meeting in Indonesian.
 Compressive sampling for variable selection taught by Nicolai Meinshausen.
 Jeremie Bigot, Problemes Sparses en statistique. "Méthodes d'Analyse Stochastique pour les COdes et Traitements NUMériques",
 Frames for the finite world: Sampling, coding and quantization, August 18 to 22 at the American Institute of Mathematics, Palo Alto, California. It is organized by: Sinan Gunturk, Goetz Pfander, Holger Rauhut, and Ozgur Yilmaz
 International Symposium on Low Power Electronics and Design 2008, National Science Seminar Complex, Indian Institute of Science, Bangalore, India, August 1113, 2008. A presentation entitled Noninvasive Leakage Power Tomography of. Integrated Circuits by Compressive Sensing will be featured.
 International Conference on Interdisciplinary Mathematical and Statistical Techniques  IMST 2008 / FIM XVI on May 1618, 2008. "Probabilistic existence theorems for group testing with lies" by Anatoly Zhigljavsky.
 Deterministic Designs with Deterministic Guarantees: Toeplitz Compressed Sensing Matrices, Sequence Designs and System Identification , Venkatesh Saligrama.
 Thomas Blumensath and Mike Davies;Iterative Hard Thresholding for Compressed Sensing. [ revised version of arXiv:0805.0510v1 ]
 ICML 2008.
 MultiTask Compressive Sensing with Dirichlet Process Priors by Yuting Qi, Dehong Liu, David Dunson, Lawrence Carin
 Compressed Sensing and Bayesian Experimental Design by Matthias W. Seeger, Hannes Nickisch
 Large Scale Approximate Inference and Experimental Design for Sparse Linear Models by Matthias Seeger.
 Hannes Nickisch has developed FWTN, a Fast WaveletTransformation for D dimensional data in L levels.
 Sparse Bayesian Nonparametric Regression by Francois Caron, Arnaud Doucet.
 Column subset selection, matrix factorization, and eigenvalue optimization by Joel Tropp.
 Consistency of Trace Norm Minimization by Francis Bach.
 Shift Invariant Sparse Coding of Image and Music Data by Morten Mørup, Mikkel Schmidt, Lars Kai Hansen. NonNegative Shift Invariant Sparse Coding algorithm is here .
 Trac Tran's presentation, Fast Efficient and Practical Algorithms for Compressed Sensing.
 Gerry Skinner, Coded Mask Imagers: when to use them, and when not ?
 Ben Kowash, Glenn Knoll
 Digital
Tomographic Imaging with TimeModulated Pseudorandom Coded Aperture and
Anger Camera by Kenneth F. Koral, W. Leslie Rogers, and Glenn F. Knoll
 Regularized Dictionary Learning for Sparse Approximation by Mehrdad YaghoobiVaighan, Thomas Blumensath and Mike Davies.
 Nonlocal Regularization of Inverse Problems by Gabriel Peyré, Sébastien Bougleux and Laurent Cohen.
 A note on compressed sensing and the complexity of matrix multiplication by Mark Iwen, Craig Spencer.
 Discriminative learned dictionaries for local image analysis by Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman.
 Sparse representations for image classification: Learning discriminative and reconstructive nonparametric dictionaries by Fernando Rodriguez and Guillermo Sapiro.
 J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Discriminative Learned Dictionaries for Local Image Analysis.
 J. Mairal, G. Sapiro and M. Elad. Multiscale sparse image representation with learned dictionaries. slides
 Ingrid Daubechies, Ronald DeVore, Massimo Fornasier, and C. Sinan Güntürk, Iteratively reweighted least squares minimization for sparse recovery.
 Remi Gribonval and Karin Schnass, Some recovery conditions for basis learning by L1minimization.
 Sparse representations of audio: from source separation to wavefield compressed sensing by Remi Gribonval.
 MixedSignal Parallel Compressed Sensing and Reception For Cognitive Radio by Zhuizhuan Yu, Sebastian Hoyos, Brian M. Sadler
 Compressed Sensing of Analog Signals, Yonina C. Eldar
 Ignace Loris, Guust Nolet, Ingrid Daubechies and Tony Dahlen, Tomographic inversion using L1norm regularization of wavelet coefficients
 Statistical approach for dictionary learning by Tieyong Zeng and Alain Trouvé
 Compressed Sensing by Holger Rauhut
 Compressive Sensing Resources
 Richard Baraniuk, Compressive sensing and an introduction to compressive sampling by Emmanuel Candès and Michael Wakin.
 The paper formerly known as "The JohnsonLindenstrauss lemma meets compressed sensing" by Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin now has the title: A simple proof of the restricted isometry property for random matrices.
 Emmanuel Candès, The restricted isometry property and its implications for compressed sensing.
 T. Tony Cai, Guangwu Xu, and Jun Zhang, On recovery of sparse signals via ell1 minimization.
 Jong Chul Ye and Su Yeon Lee, Noniterative exact inverse scattering using simultanous orthogonal matching pursuit (SOMP).
 Jianwei Ma, Compressed sensing by inverse scale space and curvelet thresholding.
 Rayan Saab, Rick Chartrand, and Özgür Yulmaz, Stable sparse approximation via nonconvex optimization.
 Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, Fundamental limits on sensing capacity for sensor networks and compressed sensing.
 Hong Jung, Kyunghyun Sung, Krishna S. Nayak, Eung Yeop Kim, and Jong Chul Ye, kt FOCUSS: A general compressed sensing framework for high resolution dynamic MRI.
 Mark Schmidt 15 different algorithms solving the LASSO problem.
 Michael Friedlander, SPGL1, examples. Matlab's publishing feature. Sparco, http://www.cs.ubc.ca/~mpf/public/BregCompare.zip (73.7K) compares L1Bregman and SPGL1
 Applying the Proximal Point Algorithm to a Non Negative Basis Pursuit Denoising model by Francois Malgouyres,Tieyong Zeng. The attendant software is here.)
 Astrocytes (more here).
 H. Jung, Kyunghyun Sung, Krishna Nayak, Eung Yeop Kim, andJong Chul Yekt FOCUSSS: a general compressed sensing framework for high resolution dynamic MRI.
 Radial singleshot STEAM MRI, Tobias Block, Jens Frahm. a poster with the same title is here.
 Martin Uecker, Thorsten Hohage, Kai Tobias Block, andJens Frahm,Image Reconstruction by Regularized Nonlinear Inversion  Joint Estimation of Coil Sensitivities and Image Content.
 SIAM IS08 meeting and EUSIPCO program
 Compressed Sensing of Audio Signals Using Multiple Sensors, Anthony Griffin (FORTH/University of Crete, Greece); Panagiotis Tsakalides (University of Crete, Greece)
 Compressive Video Sampling, Vladimir Stankovic (University of Strathclyde, United Kingdom); Lina Stankovic (University of Strathclyde, United Kingdom); Samuel Cheng (University of Oklahoma, USA)
 Compressive Coded Aperture Video Reconstruction, Roummel Marcia (Duke University, USA); Rebecca Willett (Duke University, USA)
 Fast compressive imaging using scrambled block Hadamard ensemble, Lu Gan (Brunel University, United Kingdom); Thong Do (The Johns Hopkins University, USA); Trac D. Tran (The Johns Hopkins University, USA)
 Dictionary identifiability from few training samples by Remi Gribonval and Karin Schnass.
 Underdetermined source separation via mixednorm regularized minimization by M. Kowalski, E. Vincent and Remi Gribonval.
 Adam Zelinski, Lawrence L. Wald, Kawin Setsompop, Vivek Goyal and Elfar Adalsteinsson, SparsityEnforced SliceSelective MRI RF Excitation Pulse Design
 Shiftinvariant dictionary learning for sparse representations: extending KSVD by Boris Mailhé, Sylvain Lesage, Remi Gribonval and Frederic Bimbot, Pierre Vandergheynst.
 Compressed Sensing of Audio Signals Using Multiple Sensors by Anthony Griffin and Panagiotis Tsakalides.
 Poster Abstract: Compressed Sensing of Audio Signals in a Wireless Sensor Network byAnthony Griffin and Panagiotis Tsakalides.
 The LNLA meeting program
 Weighted Superimposed Codes and Constrained Integer Compressed Sensing by Wei Dai and Olgica Milenkovic.
 Compressive Sampling of Binary Images by Vladimir Stankovic, Lina Stankovic, and Samuel Cheng.
 Shamgar Gurevich, Ronny Hadani, and Nir Sochen, On some deterministic dictionaries supporting sparsity.
 Patrick Combettes and Valérie Wajs, Signal recovery by proximal forwardbackward splitting.
 A variational formulation for framebased inverse problems by Caroline Chaux, Patrick Combettes, JeanChristophe Pesquet, and Valérie Wajs.
 Proximal thresholding algorithm for minimization over orthonormal bases by Patrick Combettes and JeanChristophe Pesquet.
 video on Integration of Sensing and Processing (December 0509, 2005) by Robert Nowak on Active learning vs. compressed sensing.
 Bregman Iterative Algorithms for Constrained Compressed Sensing and Related Problems,Wotao Yin, Stanley Osher, Donald Goldfarb, Jerome Darbon . You need to request it from the site ( here).
 Interiorpoint methods for nuclear norm minimization by Zhang Liu and Lieven Vandenberghe , CVXOPT
 SPGL1, Probing the Pareto Frontier for basis pursuit solutions (pdf).
 Andriyan Suksmono blog's name is Chaotic Pearls: latest entry
 A Compressive SFCWGPR System (also here) by Andriyan Suksmono, Endon Bharata, A. Andaya Lestari, A. Yarovoy, and L.P. Ligthart.
 Laurent Jacques, Le Petit Chercheur Illustré, Yet another signal processing (and applied math). Historical perspective on the Matching Pursuit technique.
 The Reinforcement Learning Blog
 Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection. by B. Girard, Nicolas Tabareau, Quang Cuong Pham, Alain Berthoz, JeanJacques Slotine.
 Compressed Sensing Videos: http://igorcarron.googlepages.com/csvideos.
 Massimo Fornasier, Advances in Numerical Harmonic Analysis and second presentation,poster and habilitation.
 Determining canonical views of 3D object using minimum description length criterion and compressive sensing method by PingFeng Chen and Hamid Krim.
 The application of Compressive Sensing technique on a stationary surveillance camera system, Jing Zheng and Eddie Jacobs.
 Neta Rabin, Alon Schclar, Valery Zheludev and Amir Averbuch. Detection of moving vehicles via dimensionality reduction . Wavelet based acoustic detection of moving vehicles.
 CVX, Michael Grant, Stephen Boyd and Yinyu Ye
 ICA Research Network International Workshop to be held on 2526 September, www.icarn.org
 Theory seminar at the Technion, Venkatesan Guruswami.
 Remi Gribonval and Morten Nielsen, On the strong uniqueness of highly sparse expansions from redundant dictionaries.
 Remi Gribonval and Morten Nielsen, Highly sparse representations from dictionaries are unique and independent of the sparseness measure.
 Rosa Figueras, Pierre Vandergheynst and Remi Gribonval, A simple test to check the optimality of a sparse signal approximation.
 Thong Do, Lu Gan, Nam Nguyen and Trac Tran, Sparsity adaptive matching pursuit algorithm for practical compressed sensing.
 Sina Jafarpour, Weiyu Xu, Babak Hassibi, and Robert Calderbank , Efficient compressed sensing using highquality expander graphs
 Dapo Omidiran and Martin J. Wainwright in Highdimensional subset recovery in noise: Sparsified measurements without loss of statistical efficiency.
 Julio Martin DuarteCarvajalino and Guillermo Sapiro, Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization.
 Vladimir Stankovic, Lina Stankovic, and Samuel Cheng, Compressive video sampling.
 Tolga Cukur, Michael Lustig, and Dwight Nishimura, Improving noncontrastenhanced steadystate free precession angiography with compressed sensing.
 Industrial Mathematics Institute Conference at University of South Carolina Theme: Compressed Sensing
 Announcement for DIMACS/DyDAn Working Group on Streaming, Coding, and Compressive Sensing: Unifying Theory and Common Applications to Sparse Signal/Data Analysis and Processing
 Andriyan Suksmono's blog's: Chaotic Pearls (in Indonesian)
 Wei Wang, Martin Wainwright, and Kannan Ramchandran,Informationtheoretic limits on sparse signal recovery: Dense versus sparse measurement matrices. Conference paper: Informationtheoretic limits on sparse signal recovery: Dense versus sparse measurement matrices.
 Pawan K. Baheti and Mark A. Neifeld, Featurespecific structured imaging
 Random projections based featurespecific structured imaging, Pawan Baheti and Mark Neifeld
 3D photography on your desk by JeanYves Bouguet.
 Adaptive featurespecific imaging: a face recognition example by Pawan K. Baheti, Mark A. Neifeld.
 Taskspecific information for imaging system analysis by Mark Neifeld, Amit Ashok, Pawan Baheti.
 Sparsity constrained regularization for multiframe image restoration by Shankar PM and Mark Neifeld.
 Adam Zelinski, Lawrence L. Wald, Kawin Setsompop, Vivek Goyal and Elfar Adalsteinsson, SparsityEnforced SliceSelective MRI RF Excitation Pulse Design.
 Treelets and Treelets Rejoinder, Ann Lee, Boaz Nadler and Larry Wasserman. A code in Matlab is here.
 Mauro Maggioni , James Bremer Jr. and Arthur Szlam, Matlab code for Diffusion Geometry and Diffusion Wavelets.
 The Split Bregman Method for L1 Regularized Problems, Tom Goldstein, Stan Osher. The associated code is here.
 Volkan Cevher, Marco Duarte and Richard Baraniuk, Distributed Target Localization via Spatial Sparsity
 Compressive Coded Aperture Video Reconstruction, Roummel Marcia and Rebecca Willett.
 Singleshot compressive imaging, Adrian Stern, Yair Rivenson and Bahram Javidi.
 Optically compressed image sensing using random aperture coding, Adrian Stern, Yair Rivenson and Bahram Javidi.
 Random projections imaging with extended spacebandwidth product , Adrian Stern and Bahram Javidi.
 Compressed imaging system with linear sensors, Adrian Stern.
 Emmanuel Candès and Benjamin Recht , Exact matrix completion by semidefinite programming [also here]
 Large Scale Approximate Inference for Bayesian Image Reconstruction and Measurement Design, Matthias W. Seeger
 Compressed Sensing by Wolfgang Dahmen
 All the presentations made at Journée représentations parcimonieuses du GDR ISIS are now available. Some are in French and some in English.
 François Malgouyres makes available his code performing the Basis Pursuit Denoising using one of these proximal solvers (PPA).
 Robustness of Compressed Sensing in Sensor Networks, Brett Hern. It can be downloaded here.
 Petros Boufounos: L1 minimization without amplitude informationTony Chan:
 TVL1 models for imaging: global optimization and geometric properties: Part I
 Ronald DeVore: Decoders for Compressed Sensing
 Selim Esedoglu: TVL1 models for imaging: global optimization and geometric properties: Part II
 Anna Gilbert: Combining geometry and combinatorics: a unified approach to sparsesignal recovery
 JeanLuc Guermond/Bojan Popov: Approximating PDEs in L1
 Alexander Kurganov: Numerical Methods for Modern Traffic Flow Models
 Gitta Kutyniok: l1Minimization and the Geometric Separation Problem
 Stanley Osher: Fast Bregman Iteration for Compressive Sensing and Sparse Denoising
 Tom Goldstein: The split Bregman method for L1regularized problems
 Alexander Petoukhov: l^1 greedy algorithm for finding solutions of underdetermined linear systems
 Justin Romberg: Architectures for Compressive Sampling
 Panagiotis Souganidis: Rates of convergence for monotone approximations of viscosity solutions
 Eitan Tadmor: L1 Techniques for Three Problems in PDEs, Numerics and Image Processing
 Jared Tanner: The surprising structure of Gaussian point clouds and its implications for signal processing
 Richard Tsai: Exploratory path planning and target detection
 Yin Zhang: Enhanced Compressive Sensing and More
 Extracting Salient Features From Less Data via l1Minimization by Wotao Yin and Yin Zhang.
 Sparsest solutions of underdetermined linear systems via minimization for . by Simon Foucart, MingJun Lai.
 Practical recipes for the model order reduction, dynamical simulation, and compressive sampling of largescale open quantum systems by John Sidles, Joseph Garbini, L. E. Harrell, Alfred Hero, Jon Jacky, Joe Malcomb, A. G. Norman and A. M. Williamson
 Jort Gemmeke and Bert Cranen, Noise reduction through Compressed Sensing.
 Classification on incomplete data: imputation is optional byJort Gemmeke.
 Noise robust digit recognition using sparse representations by Jort Gemmeke and Bert Cranen.
 Accelerating dynamic spiral MRI by algebraic reconstruction from undersampled kt space byT. Shin, J.F. Nielsen, Krishna S. Nayak.
 Accelerated spiral Fourier velocity encoded imaging by João Luiz Azevedo de Carvalho, Krishna S. Nayak.
 L1minimization methods for Hamilton–Jacobi equations: the onedimensional case by JeanLuc Guermond, Bojan Popov.
 Tim Lin and Felix Herrmann, Compressed wavefield extrapolation
Lawrence Carin, Dehong Liu and B. Guo, In Situ Compressive Sensing for MultiStatic Scattering : Imaging and the Restricted Isometry Property,
Henry Pfister, Fan Zhang, Compressed Sensing and Linear Codes over Real Numbers.The presentation slides are here.
 Gradient Pursuit for NonLinear Sparse Signal Modelling by Thomas Blumensath and Mike Davies.
 Marco F. Duarte, Shriram Sarvotham, Dror Baron, Michael Wakin and Richard Baraniuk, Performace Limits for Jointly Sparse Signals via Graphical Models (and the attendant Poster).
 In A Direct Formulation for Sparse PCA Using Semidefinite Programming, Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert Lanckriet ( DSPCA source code
 Laurent Jacques and Christophe De Vleeschouwer, A Geometrical Study of Matching Pursuit Parametrization.
 Alexandre d'Aspremont, Subsampling Algorithms for Semidefinite Programming.
 Understanding camera tradeoffs through a Bayesian analysis of light field projections by Anat Levin, William T. Freeman, and Fredo Durand.
 Allen Yang, lecture 1: Classification via Sparse Representation
 Allen Yang, lecture 2: Classification of Mixture Subspace Models via Sparse Representation,
 Allen Yang, lecture 3: Distributed Pattern Recognition
 Muthu MuthukrishnanCompressed sensing: L0, L2 and no L1.
 James LeeKernels of random sign matrices
 Compressive Sampling and Lossy Compression, Vivek Goyal, Alyson Fletcher and Sundeep Rangan,
 Denoising by Sparse Approximation: Error Bounds Based on RateDistortion , Alyson Fletcher, Sundeep Rangan, and Vivek Goyal, and Kannan Ramchandran.
 On the RateDistortion Performance of Compressed Sensing, Alyson Fletcher, Sundeep Rangan, and Vivek Goyal.
 RateDistortion Bounds for Sparse Approximation, Alyson Fletcher, Sundeep Rangan, and Vivek Goyal.
 Multichannel Sampling of Parametric Signals with a Successive Approximation Property, Julius Kusuma and Vivek Goyal.
 Haris Vikalo, Farzad Parvaresh, S. Misra, and Babak Hassibi in Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays.
 Compressive Sensing on a CMOS Separable Transform Image Sensor. Ryan Robucci, Leung Kin Chiu, Jordan Gray, Justin Romberg, Paul Hasler, David V. Anderson.
 LowPower Analog Image Processing using Transform Imagers by Paul Hasler, Abhishek Bandyopadhyay, and David V. Anderson
 Abhishek Bandyopadhyay's Ph.D. thesis on "Matrix Transform Imager Architecture for OnChip LowPower Image Processing" can be found here.
 UWB Echo Signal Detection With UltraLow Rate Sampling Based on Compressed Sensing by Shi, G. Lin, J. Chen, X. Qi, F. Liu, D. Zhang, L.
 A New Condition for Exact ℓ1 Recovery by Charles Dossal and Gabriel Peyre
 Frédéric Barbaresco gave me the permission to post both the presentation and the preprint. The preprint is entitled: Innovative Tools for Radar Signal Processing Based on Cartan’s Geometry of SPD Matrices & Information Geometry
 Iterative Hard Thresholding for Compressed Sensing, also at arXiv.
 Stagewise Weak Gradient Pursuits. Part I: Fundamentals and Numerical Studies.
 Stagewise Weak Gradient Pursuits. Part II: Theoretical Properties.
 A necessary and sufficient condition for exact recovery by l_1 minimization, Charles Dossal.
 Edwin Marengo, Ronald Hernandez, Y.R. Citron, Fred Gruber, M. Zambrano, and Hanoch LevAriCompressive sensing for inverse scattering.
 Edwin Marengo has also released the following preprints/talks on the subject:
 Compressive sensing and signal subspace methods for inverse scattering including multiple scattering
 Subspace and Bayesian compressive sensing methods in imaging
 Inverse scattering by compressive sensing and signal subspace methods
 Yoav Sharon, John Wright and Yi Ma , Computation and Relaxation of Conditions for Equivalence between l1 and l0 Minimization.
 James Lee has a blog .
 Arjun Hari, FPGA Based Image Reconstruction.
 Roummel Marcia and Rebecca Willett who introduced us to Compressive Coded Aperture Superresolution Image Reconstruction. Now the slides are here.
 Ashok Veeraraghavan, Ramesh Raskar, Amit Agrawal, Rama Chellappa, Ankit Mohan and Jack Tumblin , Nonrefractive modulators for encoding and capturing scene appearance and depth.
 G. Hosein Mohimani, Massoud BabaieZadeh and Christian Jutten, Fast Sparse Representation based on Smoothed l0 norm.
 Linearized Bregman Iterations for Compressed Sensing, JianFeng Cai, Stanley Osher and Zuowei Shen.
 Norms of random submatrices and sparse approximation, Joel Tropp.
 On the Conditioning of Random Subdictionaries, Joel Tropp.
 Anna Gilbert, Martin Strauss, and Joel Tropp , A Tutorial on Fast Fourier Sampling.
 A Deterministic Sublinear Time Sparse Fourier Algorithm via Nonadaptive Compressed Sensing Methods, Mark Iwen.
 Performance of the l0 approximation in a general dictionary , Francois Malgouyres, Mila Nikolova.
 Lee Potter, Phil Schniter, and Justin ZinielSparse Reconstrution for Radar.
 Fast Compressive Sampling With Structurally Random Matrices, Thong T. Do, Trac Tran and Lu Gan
 Radu Berinde, Anna Gilbert, Piotr Indyk, Howard Karloff and Martin Strauss :Combining Geometry And Combinatorics: A Unified Approach to Sparse Signal Recovery ( but also here).
 , Stephane Chretien, An Alternating l_1 Relaxation for compressed sensing.
 Edo Liberty, Nir Ailon, Amit Singer, Fast Random Projections using Lean Walsh Transforms.
 Necessary and Sufficient Conditions on Sparsity Pattern Recovery, Alyson Fletcher, Sundeep Rangan, and Vivek Goyal.
 Erik Bresch, YoonChul Kim, Krishna S. Nayak, Dani Bird, and Shrikanth Narayanan, Seeing Speech: Capturing Vocal Tract Shaping using Realtime Magnetic Resonance Imaging (also here).
 Ankit Mohan, Xiang Huang, Ramesh Raskar and Jack Tumblin in Sensing Increased Image Resolution Using Aperture Masks.
 Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm, Vincent Yan Fu Tan, Vivek Goyal.
 Terahertz Imaging with Compressed Sensing and Phase Retrieval, Wai Lam Chan, Matthew Moravec, Richard Baraniuk, and Daniel Mittleman.
 Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets, GuangHong Chen, Jie Tang, and Shuai Leng
 Compressed sensing for resolution enhancement of hyperpolarized (13)C flyback 3DMRSI. Hu S, Lustig M, Chen AP, Crane J, Kerr A, Kelley DA, Hurd R, Kurhanewicz J, Nelson SJ, Pauly JM, Vigneron DB,
 Stephane Chretien on Tighter relaxations for the sparsest recovery problem
 Babak Hassibi, Benjamin Recht, and Weiyu XuNecessary and Sufficient Condtions for Success of the Nuclear Norm Heuristic for Rank Minimization.
 Terry Tao's blog: Lewis lectures and On the permanent of a random Bernoulli matrix.
 Following ChapterZero links, I found these lecture notes by Roman Vershynin on Deanna Needel's page. The course is entitled: NonAsymptotic Random Matrix Theory
 Lecture 1: Background, Techniques, Methods
 Lecture 2: Concentration of Measure
 Lecture 3: Concentration of Measure (cont'd)
 Lecture 4: Dimension Reduction
 Lecture 5: Subgaussian Random Variables
 Lecture 6: Norm of a Random Matrix
 Lecture 7: Largest, Smallest, Singular Values of Random Rectangular Matrices
 Lecture 8: Dudley's Integral Inequality
 Lecture 9: Applications of Dudley's Inequality  Sharper Bounds for Random Matrices
Lecture 10: Slepian's Inequality  Sharpness Bounds for Gaussian Matrices  Lecture 11: Gordon's Inequality
 Lecture 12: Sudakov's Minoration
 Lecture 13: Sections of Convex Sets via Entropy and Volume
 Lecture 14: Sections of Convex Sets via Entropy and Volume (cont'd)
 Lecture 15: Invertibility of Square Gaussian Matrices, Sparse Vectors
 Lecture 16: Invertibility of Gaussian Matrices and Compressible/Incompressible Vectors
 Lecture 17: Invertibility of Subgaussian Matrices  Small Ball Probability via the Central Limit Theorem
 Lecture 18: Strong Invertibility of Subgaussian Matrices and Small Ball Probability via Arithmetic Progression
 Lecture 19: Small Ball Probability via SumSets
 Lecture 20: The Recurrence Set (Ergodic Approach)
 Suresh Venkatasubramanian and Piotr Indyk on the concentration of measure
 Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, Fundamental Limits on Sensing Capacity for Sensor Networks and Compressed Sensing.
 Remi Gribonval in his Habilitation a Diriger Des Recherches.
 Sur quelques problèmes mathématiques de modélisation parcimonieuse.[The presentation slides are here (and they are in English)]
 MoTIF : an Efficient Algorithm for Learning Translation Invariant Dictionaries, Philippe Jost, Pierre Vandergheynst, Sylvain Lesage, and Rémi Gribonval.
 Learning MultiModal Dictionaries , Holger Rauhut, Karin Schnass, Gianluca Monaci, Philippe Jost, Pierre Vandergheynst, Boris Mailhé, Sylvain Lesage, and Rémi Gribonval.
 Sylvain Lesage's Ph.D thesis and defense (100 MB ppt) (both in French) .
 Atoms of all channels, unite! Average case analysis of multichannel sparse recovery using greedy algorithms by Rémi Gribonval , Holger Rauhut, Karin Schnass, Pierre Vandergheynst.
 SparSpec : a new method for fitting multiple sinusoids with irregularly sampled data, Sebastien Bourguignon, Hervé Carfantan and Torsten Böhm
 Sparse Representationbased Image Deconvolution by Iterative Thresholding, Jalal Fadili and JeanLuc Starck.
 Image Deconvolution under Poisson Noise using Sparse Representations and Proximal Thresholding Iteration by FrancoisXavier Dupe , Jalal Fadili and JeanLuc Starck.
 Fast Sparse Representation using Smoothed L0 Norm, G. Hosein Mohimani, Massoud BabaieZadeh and Christian Jutten.
 Bayesian Experimental Design for Compressed Sensing, Hannes Nickisch and Matthias Seeger.
 Namrata VaswaniKalman Filtered Compressed Sensing.
 Sadegh Jokar, Volker Mehrmann, Marc Pfetsch and Harry YserentantSparse Approximate Solution of Partial Differential Equations.
 Sadegh Jokar and Marc PfetschExact and Approximate Sparse Solutions of Underdetermined Linear Equations.
 Computation and Relaxation of Conditions for Equivalence between l1 and l0 Minimization, Yoav Sharon, John Wright and Yi Ma.
 Roummel Marcia and Rebecca WillettCompressive Coded Aperture Superresolution Image Reconstruction.
 Mohan Shankar, Rebecca Willett, Nikos Pitsianis, Timothy Schulz, Robert Gibbons, Robert Te Kolste, J. Carriere, C. Chen, D. Prather, David Brady write about TOMBO: Thin infrared imaging systems through multichannel sampling.
 Ashwin Wagadarikar, Renu John, Rebecca Willett, and David Brady : Single disperser design for coded aperture snapshot spectral imaging,
 Blog of Andrés CorradaEmmanuel entitled De Rerum Natura: http://www.corrada.com/blog/category/compressedsensing/
 Autonomous geometric precision error estimation in lowlevel computer vision tasks, Andrés CorradaEmmanuel and Howard Schultz.
 Yuting Qi, Dehong Liu, David Dunson and Lawrence Carin , Bayesian MultiTask Compressive Sensing with Dirichlet Process Priors.
 Holger RauhutStability Results for Random Sampling of Sparse Trigonometric Polynomials.
 Mihailo Stojnic, Farzad Parvareshand Babak Hassibi, On the reconstruction of blocksparse signals with an optimal number of measurements
 Thierry Blu, PierLuigi Dragotti, Martin Vetterli, Pina Marziliano, Lionel Coulot in Sparse Sampling of Signal Innovations.
 WaveletDomain Compressive Signal Reconstruction Using a Hidden Markov Tree Model by Marco Duarte, Michael Wakin and Richard Baraniuk ( slides )
 Lorne Applebaum, Stephen Howard, Stephen Searle and Robert Calderbank in Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery.
 Computation and Relaxation of Conditions for Equivalence between l1 and l0 Minimization , Allen Yang, John Wright and Yi Ma.
 Lu Gan, Thong Do and Trac TranFast compressive imaging using scrambled block Hadamard ensemble.
 Terry Tao “The Dantzig selector: Statistical estimation when p is much larger than n" a paper he published with Emmanuel Candés.
 Petros Boufounos, Richard Baraniuk, Reconstructing sparse signals from their zero crossings.
 Jianwei MaCompressed sensing for surface metrology.
 SPARCO ( by Ewout van den Berg, Michael P. Friedlander, Gilles Hennenfent, Felix J. Herrmann, Rayan Saab, and Ozgur Yilmaz)
 Michael Friedlander and Kathrin HatzComputing nonnegative tensor factorizations.
 Deanna Needell and Joel TroppCoSaMP: Iterative signal recovery from incomplete and inaccurate samples.
 Venkat ChandarA negative result concerning explicit matrices with the restricted isometry property.
 Rachel Ward on Cross validation in compressed sensing via the Johnson Lindenstrauss lemma
 Stephen Howard, Robert Calderbank, and Stephen Searle in A fast reconstruction algorithm for deterministic compressive sensing using second order ReedMuller codes.
 There is a followon on GRIP by Jarvis Haupt, Robert Nowak . A generalized restricted isometry property.
 Georg Taubock and Franz HlawatschA compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots.
 Christopher Rozell, Don Johnson, Rich Baraniuk and Bruno OlshausenSparse Coding via Thresholding and Local Competition in Neural Circuits.
 Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity by Wei Dai and Olgica Milenkovic
 New insights into onenorm solvers from the pareto curve, Gilles Hennenfent, Ewout van den Berg,
 Michael Friedlander and Felix Herrmann was closed. It is now viewable here.
 Florian Sebert, Leslie Ying, and Yi Ming Zou entitled Toeplitz Block Matrices in Compressed Sensing.