Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing

Par : Jean-François Giovannelli, Jérôme Idier
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  • Nombre de pages299
  • FormatGrand Format
  • PrésentationRelié
  • Poids0.64 kg
  • Dimensions16,3 cm × 24,2 cm × 2,2 cm
  • ISBN978-1-84821-637-2
  • EAN9781848216372
  • Date de parution01/02/2015
  • CollectionDigital Signal and Image Proce
  • ÉditeurISTE éditions

Résumé

The solution of inverse problems is an unavoidable step in the signal and image processing chain, situated between data acquisition and decision making. Various inversion problems are presented by the authors of this book, such as tomography, detection-estimation, multi-resolution analysis, moving object tracking, and sparse approximations. Recent mathematical concepts and tools in information theory, optimization, Bayesian inference and statistical modeling are put into practice in these different contexts.
Each chapter is devoted to a widely developed and documented application. Through various perspectives, a vast number of fields is covered : medical and biological imaging, astronomy, non-destructive evaluation, video processing, digital communications, sensor networks, etc.
Jean-François Giovannelli is Professor at the University of Bordeaux in France and carried out research at the IMS laboratory into signal and image processing. His contributions concern inverse problems, deterministic and Bayesian regularization and in particular myopic and unsupervised aspects. Jérôme Idier is CNRS Director of Research at IRCCyN in Nantes, France. He is a member of the French national committee for scientific research.
His research work concerns inference and optimization for the solution of inverse problems in signal and image processing.