Statistical Methods for Image Registration and Denoising - Tapa blanda

Sambora, Matthew D.

 
9781288286348: Statistical Methods for Image Registration and Denoising
Tapa blanda
ISBN 10: 1288286341 ISBN 13: 9781288286348
Editorial: BiblioScholar, 2012

Sinopsis

This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the Barankin bound is a better predictor of shift estimator performance. The research also looks at the related problem of single-frame image denoising using block-based methods. The research introduces three algorithms that operate by identifying regions of interest within a noise-corrupted image and then generating noise free estimates of the regions as averages of similar regions in the image.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the Barankin bound is a better predictor of shift estimator performance. The research also looks at the related problem of single-frame image denoising using block-based methods. The research introduces three algorithms that operate by identifying regions of interest within a noise-corrupted image and then generating noise free estimates of the regions as averages of similar regions in the image.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
BiblioScholar
Año de publicación
2012
Idioma
Inglés
ISBN 10 
1288286341
ISBN 13 
9781288286348
Encuadernación
Tapa blanda
Número de páginas
156
Contacto del fabricante
no disponible
Persona responsable
no disponible

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