Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)

09/14/2016
by   V. Abrol, et al.
0

The third edition of the "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) took place in Aalborg, the 4th largest city in Denmark situated beautifully in the northern part of the country, from the 24th to 26th of August 2016. The workshop venue was at the Aalborg University campus. One implicit objective of this biennial workshop is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For this third edition, iTWIST'16 gathered about 50 international participants and features 8 invited talks, 12 oral presentations, and 12 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing (e.g., optics, computer vision, genomics, biomedical, digital communication, channel estimation, astronomy); Application of sparse models in non-convex/non-linear inverse problems (e.g., phase retrieval, blind deconvolution, self calibration); Approximate probabilistic inference for sparse problems; Sparse machine learning and inference; "Blind" inverse problems and dictionary learning; Optimization for sparse modelling; Information theory, geometry and randomness; Sparsity? What's next? (Discrete-valued signals; Union of low-dimensional spaces, Cosparsity, mixed/group norm, model-based, low-complexity models, ...); Matrix/manifold sensing/processing (graph, low-rank approximation, ...); Complexity/accuracy tradeoffs in numerical methods/optimization; Electronic/optical compressive sensors (hardware).

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Table of contents

  • “Sparse matrix factorization for PSFs field estimation”(p. Table of contents)
    F. Ngolè (CEA Saclay, France) and J.-L. Starck (CEA Saclay, France).

  • “Sparse BSS with corrupted data in transformed domains”.(p. Table of contents)
    C. Chenot (IRFU, CEA, France) and J. Bobin (IRFU, CEA, France).

  • “Randomness is sufficient! Approximate recovery from CS samples”.(p. Table of contents)
    V. Abrol (SCEE, IIT Mandi, India), P. Sharma (SCEE, IIT Mandi, India) and Anil K. Sao (SCEE, IIT Mandi, India).

  • “The Effect of Atom Replacement Strategies on Dictionary Learning”.(p. Table of contents)
    Paul Irofti (U. Politehnica Bucharest, Romania).

  • “Blind Deconvolution of PET Images using Anatomical Priors”.(p. Table of contents)
    Stéphanie Guérit (UCLouvain, Belgium), Adriana González (UCLouvain, Belgium) (UCLouvain, Belgium), Anne Bol (UCLouvain, Belgium), John A. Lee (UCLouvain, Belgium) and Laurent Jacques (UCLouvain, Belgium).

  • “A Non-Convex Approach to Blind Calibration for Linear Random Sensing Models”.(p. Table of contents)
    Valerio Cambareri (UCLouvain, Belgium) and Laurent Jacques (UCLouvain, Belgium).

  • “Sparse Support Recovery with Data Fidelity”.(p. Table of contents)
    Kévin Degraux (UCLouvain, Belgium), Gabriel Peyré (CNRS, Ceremade, U. Paris-Dauphine, France), Jalal M. Fadili (ENSICAEN, UNICAEN, GREYC, France) and Laurent Jacques (UCLouvain, Belgium).

  • “Low Rank and Group-Average Sparsity Driven Convex Optimization
    for Direct Exoplanets Imaging”.
    (p. Table of contents)
    Benoît Pairet (UCLouvain, Belgium), Laurent Jacques (UCLouvain, Belgium), Carlos A. Gomez Gonzalez (ULg, Belgium), Olivier Absil (ULg, Belgium).

  • “A fast algorithm for high-order sparse linear prediction”.(p. Table of contents)
    Tobias Lindstrøm Jensen (Aalborg Universitet, Denmark), Daniele Giacobello (DTS Inc., Calabasas, CA, USA), Toon van Waterschoot (KULeuven, Belgium), Mads Græsbøll Christensen (Aalborg Universitet, Denmark).

  • “Compressive Hyperspectral Imaging with Fourier Transform Interferometry”.

    (p. Table of contents)
    A. Moshtaghpour (UCLouvain, Belgium), K. Degraux (UCLouvain, Belgium), V. Cambareri (UCLouvain, Belgium), A. Gonzalez (UCLouvain, Belgium), M. Roblin (Lambda-X, Belgium), L. Jacques (UCLouvain, Belgium), and P. Antoine (Lambda-X, Belgium).

  • “Inferring Sparsity: Compressed Sensing using Generalized
    Restricted Boltzmann Machines”.
    (p. Table of contents)
    Eric W. Tramel (Ecole Normale Supérieure, PSL Research University, France), Andre Manoel (Ecole Normale Supérieure, PSL Research University, France), Francesco Caltagirone (INRIA Paris), Marylou Gabrié (Ecole Normale Supérieure, PSL Research University, France) and Florent Krzakala (Ecole Normale Supérieure, PSL Research University, France).

  • Interpolation on manifolds using Bézier functions”.

    (p. Table of contents)
    Pierre-Yves Gousenbourger (UCLouvain, Belgium), P.-A. Absil (UCLouvain, Belgium), Benedikt Wirth (U. Münster, Germany) and Laurent Jacques (UCLouvain, Belgium).

  • “Reliable recovery of hierarchically sparse signals”.(p. Table of contents)
    Ingo Roth (Freie Universität Berlin, Germany), Martin Kliesch (Freie Universität Berlin, Germany), Gerhard Wunder (Freie Universität Berlin, Germany), and Jens Eisert (Freie Universität Berlin, Germany).

  • “Minimizing Isotropic Total Variation without Subiterations”.(p. Table of contents)
    Ulugbek S. Kamilov (MERL, USA).

  • “Learning MMSE Optimal Thresholds for FISTA”.(p. Table of contents)
    Ulugbek S. Kamilov (MERL, USA) and Hassan Mansour (MERL, USA).

  • “The best of both worlds: synthesis-based acceleration
    for physics-driven cosparse regularization”.
    (p. Table of contents)
    Srđan Kitić (Technicolor R&D, France), Nancy Bertin (CNRS - UMR 6074, France) and Rémi Gribonval (Inria, France).

  • “A Student-t based sparsity enforcing hierarchical prior for linear inverse problems
    and its efficient Bayesian computation for 2D and 3D Computed Tomography”.
    (p. Table of contents)
    Ali Mohammad-Djafari (CentraleSupélec-U. Paris Saclay, France), Li Wang (CentraleSupélec-U. Paris Saclay, France), Nicolas Gac (CentraleSupélec-U. Paris Saclay, France) and Folkert Bleichrodt (CWI, The Netherlands).

  • “Simultaneous reconstruction and separation in a spectral CT framework”.(p. Table of contents)
    S. Tairi (CPPM, France), S. Anthoine (Aix Marseille Université, France), C. Morel (CPPM, France) and Y. Boursier (CPPM, France).

  • “Debiasing incorporated into reconstruction of low-rank modelled dynamic MRI data”.(p. Table of contents)
    Marie Daňková (Brno University of Technology & Masaryk University, Czech Republic) and Pavel Rajmic (Brno University of Technology, Czech Republic).

  • “Sparse MRI with a Markov Random Field Prior for the Subband Coefficients”.(p. Table of contents)
    Marko Panić (University of Novi Sad, Serbia), Dejan Vukobratovic (University of Novi Sad, Serbia), Vladimir Crnojević (University of Novi Sad, Serbia) and Aleksandra Pižurica (Ghent University, Belgium).

  • “Active GAP screening for the LASSO”.(p. Table of contents)
    A. Bonnefoy (Aix Marseille Université, France) and S. Anthoine (Aix Marseille Université, France).

  • “Paint Loss Detection in Old Paintings by Sparse Representation Classification”.(p. Table of contents)
    Shaoguang Huang (Ghent University, Belgium), Wenzhi Liao (Ghent University, Belgium), Hongyan Zhang (Wuhan University, China) and Aleksandra Pižurica (Ghent University, Belgium).

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