Introduction to Inverse Problems in Imaging

Fully updated throughout, with several new chapters, this second edition of Introduction to Inverse Problems in Imaging guides advanced undergraduate and graduate students in physics, computer science, mathematics and engineering through the principles of linear inverse problems.

Table des matières

1. Introduction. 2. Examples of image blurring. 3. The ill-posedness of image deconvolution. 4. Quadratic tikhonov regularization. 5. Iterative regularization methods. 6. Examples of linear inverse problems. 7. Singular value decomposition (SVD). 8. Inversion methods revisited. 9. Edge-preserving regularization. 10. Sparsity-enforcing regularization. 11. Statistical approaches to linear inverse problems 12. Statistical methods in the case of additive Gaussian noise 13. Statistical methods in the case of Poisson data 14. Conclusions

A propos de l'auteur

Mario Bertero is a Professor at the Università di Genova.
Patrizia Boccacci is a Professor at the Università di Genova.
Christine De Mol is a Professor at the Université libre de Bruxelles.

Résumé

Fully updated throughout, with several new chapters, this second edition of Introduction to Inverse Problems in Imaging guides advanced undergraduate and graduate students in physics, computer science, mathematics and engineering through the principles of linear inverse problems.

Détails du produit

• Auteurs: M. Bertero, M. (University of Genova Bertero, P. Boccacci, Boccacci P., Christine De Mol
• Edition: Taylor & Francis Ltd.
• Langues: Anglais
• Format d'édition: Livre Relié
• Sortie: 18.01.2022
• EAN: 9780367470050
• ISBN: 978-0-367-47005-0
• Pages: 342
• Catégories: Sciences naturelles, médecine, informatique, technique > Physique, astronomie > Général, dictionnaires; SCIENCE / Physics / General, SCIENCE / Applied Sciences, SCIENCE / Physics / Mathematical & Computational, TECHNOLOGY & ENGINEERING / Imaging Systems, SCIENCE / Scientific Instruments, SCIENCE / Laboratory Techniques, Optical physics, Applied optics, Applied mathematics