Compressive Sensing Videos

List of videos relevant to Compressive Sensing featured in Nuit Blanche

Where is my popcorn ?

  • Andrea Montanari on Iterative Algorithms.
  • A video of Tammy Kolda on Scalable Tensor Factorizations with Incomplete Data can be found here ( or downloaded here (197 MB) (the videos are also here)
  • Xampling hardware featured on video (Technion, Moshe Mishali and Yonina Eldar):
  • Compressive Sensing for Computer Vision: Hype vs Hope by Rama Chellappa.
  • A presentation at Google by Dror Baron on A Single-letter Characterization of Optimal Noisy Compressed Sensing.
  • Machine Learning Summer School on Theory and Practice of Computational Learning, MLSS09 (the first three videos are 3 hours long)

    Svetlana Avramov-Zamurovic has a new video introducing Compressive Sensing but it is in Serbian. The slides are here whereas the video is here (after 30 minutes into the video the discussion goes into CS)

    The Sparsity in Machine Learning meeting 2009 featured several presentations related to Compressive Sensing:

    Svetlana Avramov-Zamurovic has some new videos of the Compressive Sensing tutorial she is giving at USNA.

    *******

    Compressive Sensing Workshop organized at Duke February 25 & 26, 2009

  • Ronald Coifman - Compressed Sensing, Intrinsic Variables and Diffusion Geometries (video)
  • Stanley Osher - Bregmanized Methods for Sparse Reconstruction and Restoration (video)
  • Rama Chellappa and Volkan Cevher - Applications of Compressed Sensing Concepts to Some Computer Vision Problems (video)
  • Guillermo Sapiro - Learning Sparse Representations to Restore, Classify and Sense Images & Video (video)
  • Martin Wainwright - Graphical Model Selection in High-dimensions: Trade-offs Between Computational and Statistical Efficiency (video)
  • Mario Figueiredo - Iterative Shrinkage/Thresholding Algorithms: Some History and Recent Development (video)
  • Lee Potter - Bayes to the Bone: Sparse Linear Regression with Limited Data (video)
  • Kevin Murphy - Learning Graph Structures with Unknown Blocks (video)
  • Robert Calderbank - Deterministic Compressive Sensing Matrices (video)
  • Rob Nowak - Distilled Sensing: The Power of Adaptivity (video)
  • Rick Chartrand - Fast Algorithms for Nonconvex Compressive Sensing (video)
  • Joel Tropp - Beyond Nyquist: Efficient Sampling of Sparse, Bandlimited Signals (video)
  • Michael Lustig - Frontiers in Rapid MRI: Parallel Imaging and Compressed Sensing (video)
  • Mark Neifeld - Adaptation for Task-Specific Compressive Sensing (video)
  • Kevin Kelly - Micromirror-based Compressive Imaging and Spectroscopy (video)
  • David Brady - Coding and Regularization in Optical Sensor Forward and Inverse Models (video)
  • Venkatesh Saligrama - Noisy Group Testing and Boolean Compressed Sensing (video)
  • Yonina Eldar - Beyond Nyquist: Compressed Sensing of Analog Signals (video)
  • Justin Romberg - Multiple Channel Estimation and Compressive Sensing (video)
  • Donald Goldfarb - Fixed Point and Bregman Iterative Methods for Matrix Rank Minimization (video)
  • Wotao Yin - Enhanced Compressed Sensing Based on Iterative Support Detection (video)
  • Thomas Blumensath - Iterative Hard Thresholding: Theory and Practice (video)
  • Richard Baraniuk - Model-based Compressive Sensing (video)
  • Rebecca Willet - Compressed Sensing in Low-Light Imaging (video)
  • George Papanicolaou - Resolution and Robustness of Array Imaging Algorithms Viewed with Compressive Sensing in Mind (video)
  • James McClellan - Compressive Sensing Data Acquisition and Imaging for GPR (video)
  • Mathias Seeger - Compressed Sensing for Medical Imaging (video)
  • Jian Li - On Sparse Bayesian Learning and Iterative Adaptive Approach (video)
  • David Wipf - Latent Variable Bayesian Models for Promoting Sparsity (video)
  • Lawrence Carin - Putting Compression into Compressive Sensing (video)
  • ***********
  • Svetlana Avramov-Zamurovic presented a tutorial on Compressive Sensing in a course at the USNA. She also made a video of it. It is here. The slides and attendant paper by Richard Baraniuk on which the presentation is based.
  • A Short Introduction to Compressed Sensing by Emmanuel Candes

  • Compressive Structured Light for Recovering Inhomogeneous Participating Media by Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi,
  • Compressive sensing for background subtraction as implemented by Raghavendra Bhat, Rita Chattopadhyay for a homework.
  • Terry Tao, Presentation on Compressed Sensing at NTNU
  • Anna Gilbert,* Applications of Compressed Sensing to Biology
  • Ben Recht,* Exact Low-rank Matrix Completion via Convex Optimization
  • Adi Akaviavideo rump, Deterministically and Locally Finding Significant Fourier Transform Coefficients.
  • Ramesh Raskar at ECTV'08. Computational Photography: Epsilon to Coded Imaging
  • Richard BaraniukManifold models for signal acquisition, compression, and processing (flv)
  • Yi Ma:Dense error correction via L1 minimization (flv)
  • Ingrid Daubechies: Mathematical problems suggested by Analog-to-Digital conversion (flv)
  • There is now a video of Richard Baraniuk showing his latest introduction to Compressed Sensing at Microsoft Research on August 4, 2008. Please be aware, that the address does not seem to work with Firefox, Chrome or Opera (I tried), it only works with Internet Explorer. The original link that can be seen from any browser is here. It eventually yields the IE only presentation.
  • Machine Learning as taught by Andrew Ng at Stanford is now on Youtube.
  • Saturday Morning Cartoon Series: Explaining Compressed Sensing (Episode 1)
  • Graham Cormode did a presentation/tutorial on Data stream algorithms last July. The video is here. The ppt slides are here while the pdf slides are here.
  • A Video presentation (25MB) of Compressive Structured Light for Recovering Inhomogeneous Participating Media by Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi



  • Autonomous Geometric Precision Error Estimation in Low-level Computer Vision Tasks
    . A work by Andres Corrada-Emmanuel and Howard Schultz presented by John Paisley from Duke.
  • 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.
  • A short Introduction to Compressed Sensing by Emmanuel Candes, a video made at ITA 08 can be watched at scivee.tv.
  • A video entitled: Compressive Sampling: A new Paradigm in Signal Acquisition in Portuguese, actually it is Reconstrução de imagens subamostradas (compressed sensing) by Mário Figueiredo.
  • Single Pixel Illumination Based Camera.
  • Dror Baron, at the Stanford University Computer Systems Colloquium. The paper: Measurements vs. Bits: Compressed Sensing meets Information Theory. The powerpoint presentation is here.
  • Presentation by Richard Baraniuk in Jackson Hole. #1
    #2
    #3
    #4
    #5
  • Remi Gribonval 's HDR presentation entitled "Sparse Representations: From Source Separation to Compressed Sensing" in a video (ram) and in an audio only format. The accompanying slides are here
  • David Brady in 2005 in a workshop on Radar and Optical Imaging that took place at IMA in 2005 where he presented a week of talks on Computational Optical Imaging and Spectroscopy. The slides are here whereas the videos are here:

  • Compressive Sampling and Frontiers in Signal Processing, IMA on June 4-15, 2007.

    Richard Baraniuk

    Emmanuel J. Candès

    • Sparsity, Talks(A/V) (ram)
      • After a rapid and glossy introduction to compressive sampling–or compressed sensing as this is also called–the lecture will introduce sparsity as a key modeling tool; the lecture will review the crucial role played by sparsity in various areas such as data compression, statistical estimation and scientific computing.
    • Sparsity and the l1 norm, Talks(A/V) (ram)
      • In many applications, one often has fewer equations than unknowns. While this seems hopeless, we will show that the premise that the object we wish to recover is sparse or compressible radically changes the problem, making the search for solutions feasible. This lecture discusses the importance of the l1-norm as a sparsity promoting functional and will go through a series of examples touching on many areas of data processing.
    • Compressive sampling: sparsity and incoherence , Talks(A/V) (ram)
      • Compressed sensing essentially relies on two tenets: the first is that the object we wish to recover is compressible in the sense that it has a sparse expansion in a set of basis functions; the second is that the measurements we make (the sensing waveforms) must be incoherent with these basis functions. This lecture will introduce key results in the field such as a new kind of sampling theorem which states that one can sample a spectrally sparse signal at a rate close to the information rate---and this without information loss.
    • The uniform uncertainty principle, Talks(A/V) (ram)
      • We introduce a strong form of uncertainty relation and discuss its fundamental role in the theory of compressive sampling. We give examples of random sensing matrices obeying this strong uncertainty principle; e.g. Gaussian matrices.
    • The role of probability in compressive sampling, Talks(A/V) (ram)
      • This lecture will discuss the crucial role played by probability in compressive sampling; we will discuss techniques for obtaining nonasymptotic results about extremal eigenvalues of random matrices. Of special interest is the role played by high- dimensional convex geometry and techniques from geometric functional analysis such as the Rudelson's selection lemma and the role played by powerful results in the probabilistic theory of Banach space such as Talagrand's concentration inequality.
    • Robust compressive sampling and connections with statistics, Talks(A/V) (ram)
      • We show that compressive sampling is–perhaps surprisingly–robust vis a vis modeling and measurement errors.
    • Robust compressive sampling and connections with statistics (continued) Talks(A/V) (ram)
      • We show that accurate estimation from noisy undersampled data is sometimes possible and connect our results with a large literature in statistics concerned with high dimensionality; that is, situations in which the number of observations is less than the number of parameters.
    • Connections with information and coding theory Talks(A/V) (ram)
      • We morph compressive sampling into an error correcting code, and explore the implications of this sampling theory for lossy compression and some of its relationship with universal source coding.
    • Modern convex optimization Talks(A/V) (ram)
      • We will survey the literature on interior point methods which are very efficient numerical algorithms for solving large scale convex optimization problems.
    • Applications, experiments and open problems Talks(A/V) (ram)
      • We discuss several applications of compressive sampling in the area of analog-to-digital conversion and biomedical imaging and review some numerical experiments in new directions. We conclude by exposing the participants to some important open problems
    Ronald DeVore

    Anna Gilbert

    • Algorithms for Compressed Sensing, ISlides (pdf), Talks(A/V) (ram)
      • What algorithmic problem do we mean by Compressed Sensing? There are a variety of alternatives, each with different algorithmic solutions (both theoretical and practical). I will discuss some of the different types of results from the combinatorial to the probabilistic.
    • Algorithms for Compressed Sensing, IILecture notes (pdf) , Talks(A/V) (ram)
      • What do these algorithms all have in common? What are the common goals of the problems and how do they achieve them? I will discuss several known techniques and open problems.
    Douglas N. Arnold


    Leon Axel (New York University) leon.axel@nyumc.org , Steen Moeller (University of Minnesota) moeller@cmrr.umn.edu

    Short presentations by participantsShort presentation by participants (A/V) (ram)
    Discussion(A/V) (ram)
    Discussion(A/V) (ram)
    Short presentations by participants

    . Video on Integration of Sensing and Processing (December 05-09, 2005) by Robert Nowak on Active learning vs. compressed sensing.

    . Richard Baraniuk hasdone two talks: one at Rice and the other at IMA.
  • web counter