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

From the application field’s point of view, a significant part of the book is devoted to conventional subjects in the field of inversion: biological and medical imaging, astronomy, non-destructive evaluation, processing of video sequences, target tracking, sensor networks and digital communications.

The variety of chapters is also clear, when we examine the acquisition modalities at stake: conventional modalities, such as tomography and NMR, visible or infrared optical imaging, or more recent modalities such as atomic force imaging and polarized light imaging.

Jean-François Giovannelli, Professor with Université de Bordeaux 1, France.

Jérôme Idier is a researcher at IRCCyN (Institut de Recherches en Cybernetique de Nantes), France.

1. 3D reconstruction in X-ray tomography: example of an approach for clinical data processing

2. Parsimonious approximation for force-volume image processing in atomic force microscopy.

3. Polarimetric optical image restoration by non-local medium

4. Video processing and regularized and inversion methods

5. Bayesian approach in performance modeling. Application to super-resolution

6. Estimation of ray spectrum for irregularly sampled signals in astrophysics

7. Joint detection-estimation in functional NRM

8. MCMC and variational approaches of Bayesian inversion in diffraction imaging

9. Variational and bi-model Bayesian approach for the reconstruction-separation of astrophysical components

10. Variational core approach for target tracking in a wireless sensor network

11. Entropies and entropic criteria

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