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WORLDCOMP'09 Tutorial: Prof. Ali Mohammad-Djafari

Last modified 2009-07-04 00:25

Inverse Problems in Imaging Systems and Computer Vision: From Deterministic Regularization to Probabilistic Bayesian Approaches
Prof. Ali Mohammad-Djafari
Directeur de recherche au CNRS
Centre National de la Recherche Scientifique (CNRS), France

Date: July 13, 2009
Time: 5:30-9:00 PM
Location: Silver Room

Abstract

Inverse problems arise in many imaging and computer vision systems: image denoising, restoration and reconstruction, super-resolution, fusion or separation. In many imaging applications such as medical imaging or non destructive testing (2D, 3D, 2D+time or 3D+time) describing the problem as an inverse problem is natural, because we have measured data which are related to the unknown quantities through a physical model. In computer vision the problems such as stereo, image fusion, 3D scene reconstruction from shadows or from photographies at different angles, satellite imaging, etc., can also easily be written as inverse problems. We can also write many problems such as Blind source separation, Compressed sensing, multi or hyper spectral image segmentation as inverse problems and parameter estimation. A common framework for all these problems can be written in an algebraic form and then easily compare the deterministic regularization theory and the probabilistic Bayesian inference frameworks.

Objectives

Give a general mathematical framework for a great number of signal and image processing problems such as: Image denoising, restoration, super-resolution, segmentation, compression, separation and classification, encountered in many application area such as: Computed Tomography (medical imaging and industrial Non Destructive Testing) and Radar and SAR imaging. The outline of the tutorial is:

Examples of inverse problems in different area and applications
Description in a common mathematical framework 3- Deterministic regularization theory
Probabilistic methods
Bayesian inference and estimation framework
Prior models: from simple separable and Markovian to complex and hierarchical Markovian models with hidden Markovian fields
A Computer tomography example where the interest of the Bayesian approach with a Gauss-Markov-Potts prior modeling is shown.

Intended Audience

All researchers interested by signal and image processing, particularly those who are familiar with inverse problems, regularization theory and probabilistic inference and statistical methods.

Biography of Instructor

Ali Mohammad-Djafari was born in Iran. He received the B.Sc. degree in electrical engineering from Polytechnique of Teheran in 1975, the Engineering diploma degree (M.Sc.) from "Ecole Supérieure d'Electricité (SUPELEC)", Gif sur Yvette, France in 1977, the "Docteur-Ingénieur" (Ph.D.) degree and Doctorat d'Etat in Physics from "Université Paris Sud 11 (UPS)", Orsay, France, respectively in 1981 and 1987. He was Associate Professor at UPS for two years (1981-1983). Since 1984, he has a permanent position at "Centre National de la Recherche Scientifique (CNRS)" and works at "Laboratoire des Signaux et Systèmes (L2S)" at "SUPELEC". From 1998 to 2002, he has been at the head of Signal and Image Processing division at this laboratory. In 1997-1998. He has been visiting Associate Professor at University of Notre Dame, Indiana, USA. Presently, he is "Directeur de recherche" and his main scientific interests are in developing new probabilistic methods based on Bayesian inference, Information theory and Maximum entropy approaches for inverse problems in general, and more specifically for signal and image reconstruction and restoration. His recent research projects contain: Blind Sources Separation (BSS) for multivariate signals (satellite images, hyper spectral images), Data and Image fusion, Super resolution, X ray Computed Tomography, Microwave imaging, SAR imaging and Spatio-temporal Positron Emission Tomography (PET) data and image processing. The main application domain of his interests are Computed Tomography (X rays, PET, SPECT, MRI, Eddy current imaging, Ultrasound, Microwave, Radar and SAR Imaging) either for medical imaging or for Non Destructive Testing (NDT) in industry.

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WORLDCOMP'09 Tutorial: Prof. Ali Mohammad-Djafari