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Invited Speakers
Title: Optimization Algorithms for Sparse Representations: Some History and Recent Developments
Professor M�rio A. T. Figueiredo
Department of Electrical and Computer Engineering
Instituto Superior T�cnico (IST)
Portugal
Convex optimization has played a central role in signal/image processing
based on sparse representations, namely in addressing inverse problems (such
as reconstruction and deconvolution) using sparsity-based regularization.
The optimization problems resulting from these sparsity-based formulations
are characterized by a very high dimensionality combined with
non-smoothness, and have stimulated much research in special purpose
algorithms for these applications.
This talk will present an historical overview of this area, from the first
algorithms proposed in the early 2000's to the most recent advances, which
are orders of magnitude faster than those early methods. In the last part of
the talk, some recent non-convex optimization techniques (namely for blind
image deconvolution) will also be addressed.
Download PDF [3 MB]
Bio »
M�rio A. T. Figueiredo received MSc and PhD degrees in electrical and
computer engineering, both from Instituto Superior T�cnico (IST), the
engineering school of the University of Lisbon, in 1990 and 1994.
Since 1994, he has been with the faculty of the Department of Electrical and
Computer Engineering, IST, where he is now a Professor.
He is also area coordinator and group leader at Instituto de
Telecomunica��es, a private non-profit research institute.
His research interests include image processing and analysis, pattern
recognition, statistical learning, and optimization. M�rio Figueiredo is a
Fellow of the IEEE and of the IAPR. He received the 1995 Portuguese IBM
Scientific Prize, the 2008 UTL/Santander-Totta Scientific Prize, the 2011
IEEE Signal Processing Society Best Paper Award, the 2014 IEEE W. R. G.
Baker Award, and several conference best paper awards. He is/was associate
editor of several journals (namely, the IEEE Transactions on Image
Processing, the IEEE Transactions on Pattern Analysis and Machine
Intelligence, the SIAM Journal on Imaging Sciences). He co-chaired the 2001
and 2003 Workshops on Energy Minimization Methods in Computer Vision and
Pattern Recognition and is the technical program chair of the 2014 European
Signal Processing Conference. He presented invited lectures in many
conferences and workshops, and served in the technical/program committees of
many international conferences.
Title: Morphological Diversities in Astrophysics
Professor Jean-Luc Starck
Head of CosmoStat Laboratory
Institute of Research into the Fundamental Laws of the Universe (IRFU)
France
We present the concept of Morphological Diversity, which is based on sparsity and allows us to separate blindly one data set into
several components, each one being sparse in a given representation. This method has been extended to multichannel data.
We show how this idea allows us to analyze differently astrophysical data set such as cosmic microwave background data provided
by WMAP and PLANCK satellites.
Download PDF [29 MB]
Bio »
Jean-Luc Starck is Senior Scientist at the Institute of Research into the Fundamental Laws
of the Universe, CEA-Saclay, France. Jean-Luc Starck has a Ph.D from Nice Observatory and an
Habilitation from University Paris XI. He was a visitor at the European Southern Observatory (ESO) in
1993, at UCLA in 2004 and at Stanford's statistics department in 2000 and 2005. He has been a
Researcher since 1994 at CEA-Saclay, Institute of Research into the Fundamental Laws of the Universe.
His research interests include cosmology, especially cosmic microwave background and weak lensing
data, and statistical methods such as wavelets and other sparse representations of data. He is leader
of the CosmoStat laboratory at CEA and he is involved in EUCLID ESA project. He has published more
than 200 papers in different areas in scientific journals and he is also author of three books
entitled Image Processing and Data Analysis: the Multiscale Approach(Cambridge University Press, 1998),
Astronomical Image and Data Analysis (Springer, 2nd edition, 2006) and Sparse Image and Signal
Processing: Wavelets, Curvelets, Morphological Diversity (Cambridge University Press, 2010). For more
information visit his personal web page at http://jstarck.free.fr.
Title: Sparse Stochastic Processes with Application to Biomedical Imaging
Professor Michael Unser
Biomedical Imaging Group
Ecole Polytechnique F�d�rale de Lausanne
Switzerland
Sparse stochastic processes are defined in terms of a generalized innovation
model: they are characterized by a whitening operator that shapes their
Fourier spectrum, and a L�vy exponent that controls their intrinsic
sparsity. Starting from the characteristic form of these processes, we
derive an extended family of Bayesian signal estimators. While our family of
MAP estimators includes the traditional methods of Tikhonov and
total-variation (TV) regularization as particular cases, it opens the door
to a much broader class of potential functions (associated with infinitely
divisible priors) that are inherently sparse and typically nonconvex. We
apply our framework to the reconstruction of magnetic resonance images and
phase-contrast tomograms and to the deconvolution of fluorescence
micrographs; we also present simulation examples where the proposed scheme
outperforms the more traditional convex optimization techniques (in
particular, TV).
Download PDF [28 MB]
Bio »
Michael Unser is professor and director of EPFL's Biomedical Imaging Group,
Lausanne, Switzerland. His primary area of investigation is biomedical image
processing. He is internationally recognized for his research contributions
to sampling theory, wavelets, the use of splines for image processing, and
stochastic processes. He has published over 200 journal papers on those
topics. He is the author with P. Tafti of the book �An introduction to
sparse stochastic processes� to be published by Cambridge University Press.
From 1985 to 1997, he was with the Biomedical Engineering and
Instrumentation Program, National Institutes of Health, Bethesda USA,
conducting research on bioimaging.
Dr. Unser has held the position of associate Editor-in-Chief (2003-2005) for
the IEEE Transactions on Medical Imaging. He is currently member of the
editorial boards of SIAM J. Imaging Sciences, IEEE J. Selected Topics in
Signal Processing, Foundations and Trends in Signal Processing, and the
Proceedings of the IEEE. He is the founding chair of the technical committee
on Bio Imaging and Signal Processing (BISP) of the IEEE Signal Processing
Society.
Prof. Unser is a fellow of the IEEE (1999), an EURASIP fellow (2009), and a
member of the Swiss Academy of Engineering Sciences. He is the recipient of
several international prizes including three IEEE-SPS Best Paper Awards and
two Technical Achievement Awards from the IEEE (2008 SPS and EMBS 2010).
Panel: Sparse Representation for Image Analysis and Recognition: Trends and Applications
Download MP4 (1/2) [78 MB]
Download MP4 (2/2) [80 MB]
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