Machine Vision and Image Processing<a href="http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=16422&copyownerid=25022" target="_new"> MVIP 2011, November 15-17, Teheran, Iran</a> 
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Keynote speaker</a>
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Title: Bayesian approach with prior models which enforce sparsity in signal and image processing
<a href="http://djafari.free.fr/Documents/MVIP_2011.txt" target="_new">Abstract</a> 


Title: 
Bayesian approach with prior models which enforce sparsity in signal and image processing

Abstract:
In this talk, first I give an overview of the Bayesian approach for inverse 
problems arising in the field of signal and image processing. 
Then, I will mention different prior modelling for signals and images
which can be used to enforse sparsity. 
Even if the sparsity can be directly on the original space or in a transformed space, 
the main tools do not change. Here we consider it directly on the original space 
(impulsive signals).
Between the possible models, we focus on some of them which are either heavy tailed
(Generalized Gaussian, Student-t or Cauchy)
or mixture models (Mixture of Gaussians, Bernouilli-Gaussian, Bernouilli-Gamma,..).
Depending on the prior model selected, the Bayesian computations (optimization
for the Joint Maximum A Posteriori (MAP) estimate or MCMC or Variational Bayes
Approximations (VBA) for Posterior Means (PM) or complete density estimation)
may become more complex. We will see that the hierarchical models are more appropriate 
for handeling more complex situations. 
We propose these models, discuss on different possible Bayesian estimators,
describe the corresponding appropriate algorithms, and discuss on their corresponding
relative complexities and performances.
We show the results of the proposed methods on some applications such as
Signal deconvolution, Sources separation or localisation, Image restoration, 
Computed Tomography with X rays or diffracted waves, etc.