Regularization methods for ill-posed poisson imaging: Theoretical justification for various regularization schemes and numerical methods for astronomical image reconstruction - Couverture souple

Dara, N'Djekornom

 
9783639701456: Regularization methods for ill-posed poisson imaging: Theoretical justification for various regularization schemes and numerical methods for astronomical image reconstruction
Couverture souple
ISBN 10 : 3639701453 ISBN 13 : 9783639701456
Editeur : Scholars' Press, 2014

Synopsis

The noise contained in images collected by a charge coupled device (CCD) camera is predominantly of Poisson type. This motivates the use of the negative logarithm of the Poisson likelihood in place of the ubiquitous least squares fit-to-data. However, if the underlying mathematical model is assumed to have the form z = Au, where A is a linear, compact operator, the problem of minimizing the negative log-Poisson likelihood function is ill-posed, and hence some form of regularization is required. This work involves solving a variational problem: minimizing the sum of the negative log Poisson likelihood and a regularizing functional. The main result of this book is a theoretical analysis of this variational problem for various regularization functionals. In addition, this work presents an efficient computational method for its solution, and the demonstration of the effectiveness of this approach in practice by applying the algorithm to simulated astronomical imaging data corrupted by the CCD camera noise model mentioned above.

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Présentation de l'éditeur

The noise contained in images collected by a charge coupled device (CCD) camera is predominantly of Poisson type. This motivates the use of the negative logarithm of the Poisson likelihood in place of the ubiquitous least squares fit-to-data. However, if the underlying mathematical model is assumed to have the form z = Au, where A is a linear, compact operator, the problem of minimizing the negative log-Poisson likelihood function is ill-posed, and hence some form of regularization is required. This work involves solving a variational problem: minimizing the sum of the negative log Poisson likelihood and a regularizing functional. The main result of this book is a theoretical analysis of this variational problem for various regularization functionals. In addition, this work presents an efficient computational method for its solution, and the demonstration of the effectiveness of this approach in practice by applying the algorithm to simulated astronomical imaging data corrupted by the CCD camera noise model mentioned above.

Biographie de l'auteur

Dr. N’Djekornom Dara Laobeul holds a PhD. in computational mathematics from the University of Montana. He was assistant at the University Of N’Djamena (CHAD) before he moved to the University of Montana where he currently teaches. His research interests include image reconstruction and correlation analysis. He has published several articles.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Scholars' Press
Date d'édition
2014
Langue
anglais
ISBN 10 
3639701453
ISBN 13 
9783639701456
Reliure
Broché
Nombre de pages
120
Coordonnées du fabricant
non disponible
Personne responsable
non disponible