Inverse Problems in Vision and 3D Tomography

Inverse Problems in Vision and 3D Tomography

Edited by

Ali Mohammad-Djafari

ISBN : 9781848211728

Publication Date : December 2009

Hardcover 480 pp

215.00 USD



The concept of an inverse problem is a familiar one to most scientists and engineers, particularly in the field of signal and image processing, imaging systems (medical, geophysical, industrial non-destructive testing, etc.) and computer vision. In imaging systems, the aim is not just to estimate unobserved images, but also their geometric characteristics from observed quantities that are linked to these unobserved quantities through the forward problem. This book focuses on imagery and vision problems that can be clearly written in terms of an inverse problem where an estimate for the image and its geometrical attributes (contours and regions) is sought.

The chapters of this book use a consistent methodology to examine inverse problems such as: noise removal; restoration by deconvolution; 2D or 3D reconstruction in X-ray, tomography or microwave imaging; reconstruction of the surface of a 3D object using X-ray tomography or making use of its shading; reconstruction of the surface of a 3D landscape based on several satellite photos; super-resolution; motion estimation in a sequence of images; separation of several images mixed using instruments with different sensitivities or transfer functions; and more.


1. Introduction to Inverse Problems in Imaging and Vision, Ali Mohammad-Djafari.
2. Noise Removal and Contour Detection, Pierre Charbonnier and Christophe Collet.
3. Blind Image Deconvolution, Laure Blanc-Féraud, Laurent Mugnier and André Jalobeanu.
4. Triplet Markov Chains and Image Segmentation, Wojciech Pieczynski.
5. Detection and Recognition of a Collection of Objects in a Scene, Xavier Descombes, Ian Jermyn and Josiane Zerubia.
6. Apparent Motion Estimation and Visual Tracking, Etienne Mémin and Patrick Pérez.
7. Super-resolution, Ali Mohammad-Djafari and Fabrice Humblot.
8. Surface Reconstruction from Tomography Data, Charles Soussen and Ali Mohammad-Djafari.
9. Gauss-Markov-Potts Prior for Bayesian Inversion in Microwave Imaging, Olivier Féron, Bernard Duchêne and Ali Mohammad-Djafari.
10. Shape from Shading, Jean-Denis Durou.
11. Image Separation, Hichem Snoussi and Ali Mohammad-Djafari.
12. Stereo Reconstruction in Satellite and Aerial Imaging, Julie Delon and Andrés Almansa.
13. Fusion and Multi-modality, Christophe Collet, Farid Flitti, Stéphanie Bricq and André Jalobeanu.

About the authors/editors

Ali Mohammad-Djafari, BSc, MSc, PhD, works at the Centre National de la Recherche Scientifique (CNRS) and Laboratoire des Signaux et Systèmes (L2S). He is currently director of research 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 in imaging and vision.