San Diego Convention Center
San Diego, California, United States
11 - 15 August 2019
Conference OP502
Wavelets and Sparsity XVIII
Important
Dates
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Abstract Due:
30 January 2019

Author Notification:
8 April 2019

Manuscript Due Date:
17 July 2019

Conference
Committee
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Conference Chairs
Program Committee
  • Sophie Achard, Gipsa-lab (France)
  • Mehmet Akçakaya, Univ. of Minnesota, Twin Cities (United States)
  • Selin Aviyente, Michigan State Univ. (United States)
  • Bernhard G. Bodmann, Univ. of Houston (United States)
  • Pierre Borgnat, Lab. de Physique (France)
  • Petros T. Boufounos, Mitsubishi Electric Research Labs. (United States)
  • Benjamin Girault, The Univ. of Southern California (United States)

Program Committee continued...
  • Vivek K. Goyal, Boston Univ. (United States)
  • Emily J. King, Univ. Bremen (Germany)
  • Gitta Kutyniok, Technische Univ. Berlin (Germany)
  • Demetrio Labate, Univ. of Houston (United States)
  • Fernanda Laezza, The Univ. of Texas Medical Branch (United States)
  • Dustin G. Mixon, The Ohio State Univ. (United States)
  • Jean-Christophe Olivo-Marin, Institut Pasteur (France)
  • Audrey Repetti, Heriot-Watt Univ. (United Kingdom)
  • Michael Unser, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
  • Yves Wiaux, Heriot-Watt Univ. (United Kingdom)

Call for
Papers
This long-running series provides a forum for presentation of results in theory and applications of sparse representations. Originally, the focus of this conference series was on wavelets. The lasting impact of wavelet theory is the rise of sparsity as a modeling paradigm that complements bandwidth and provides tremendous opportunities in representation, estimation, and acquisition of signals. Accordingly, in the course of many successful meetings the topics have expanded and now encompass the entire domain of the theory and applications of all signals that have sparse representations or approximations. The conference welcomes original papers on the mathematics of signal and image processing and analysis and in all areas of mathematical and computational sciences that are affected fundamentally by the choice of image/signal representation. The series distinguishes itself by successfully straddling disciplinary boundaries; it has drawn preeminent researchers in mathematics, signal and image processing and analysis, computer vision, medical imaging, neuroscience, physics, and other fields. It focuses on novel applications of signal and image analysis, data and network science by refinements of existing techniques, and new theoretical developments.

Please visit www.waveletseries.org for a history of this event.

Topics for submission may include (but are not limited to):
  • sparsity and big data
  • high-dimensional signal estimation
  • algorithms for convex and non-convex optimizations
  • learning-based signal and image processing
  • signal processing and representations on graphs
  • sparsity in manifold learning
  • compressed or compressive sensing
  • scattering transform and harmonic analysis of deep neural networks
  • variational techniques and optimization for compressed or compressive sensing
  • algorithms for estimation and detection of sparse signals, including finite rate of innovation
  • time-frequency analysis
  • multiscale methods for super-resolution
  • multiscale random processes
  • sparse Poisson intensity reconstructions
  • multiresolution surface representations and graphics
  • vwavelets and atomic representations for approximations
  • wavelet theory and multirate filterbanks
  • regular and irregular sampling
  • overcomplete representations in finite- and infinite-dimensional spaces
  • new constructions of bases and frames for sparse representations
  • Sigma-Delta quantization
  • sparsity in MRI reconstructions
  • atomic and sparse representations with applications in physics, neuroscience, geosciences, imaging, computational geometry, biometrics, communications, radar, sonar, etc.
Note: Please follow the following submission instructions carefully: Submit an extended abstract of 2 pages including as many figures as needed (in addition to the 250 word text-only abstract required by SPIE), and include a summary cover sheet that includes:
  • description of the problem addressed: why is it important?
  • description of the original contribution of this work: how does it compare with previous work on the problem and work on similar problems?
  • no short author bios are needed.
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