San Diego Convention Center
San Diego, California, United States
6 - 10 August 2017
Conference 10394
Wavelets and Sparsity XVII
Sunday - Wednesday 6 - 9 August 2017
This conference is no longer accepting submissions.
Late submissions may be considered subject to chair approval. For more information, please contact Megan Artz.
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Abstract Due:
23 January 2017

Author Notification:
3 April 2017

Manuscript Due Date:
10 July 2017

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Conference Chairs
Program Committee
Program Committee continued...
  • Mathews Jacob, The Univ. of Iowa (United States)
  • Gitta Kutyniok, Technische Univ. Berlin (Germany)
  • Demetrio Labate, Univ. of Houston (United States)
  • Fernanda Laezza, The Univ. of Texas Medical Branch (United States)
  • Jean-Christophe Olivo-Marin, Institut Pasteur (France)
  • Audrey Repetti, Heriot-Watt Univ. (United Kingdom)
  • Naoki Saito, Univ. of California, Davis (United States)
  • Michael Unser, Ecole Polytechnique Fédérale de Lausanne (Switzerland)
  • Yves Wiaux, Heriot-Watt Univ. (United Kingdom)
  • Jong Chul Ye, KAIST (Korea, Republic of)

Call for
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 image analysis and processing methods, refinements of existing techniques, and new theoretical developments.

Please visit 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
  • sparsity in MRI reconstructions
  • variational techniques and optimization for compressed or compressive sensing
  • finite rate of innovation
  • multiscale methods for super-resolution
  • multiscale random processes
  • sparse Poisson intensity reconstructions
  • overcomplete representations in finite- and infinite-dimensional spaces
  • new constructions of bases and frames for sparse representations
  • atomic and sparse representations with applications in physics, neuroscience, geosciences, biomedical imaging, computational geometry, and biometrics
  • multiresolution surface representations and graphics
  • wavelets and atomic representations for approximations
  • regular and irregular sampling
  • applications in communications, radar, sonar, imaging, etc.
  • algorithms for estimation and detection of sparse signals
  • Sigma-Delta quantization
  • time-frequency analysis
  • wavelet theory and multirate filterbanks.
Note: Please follow the submission instructions below 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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