Statistical Methods for Image Registration and Denoising

Matthew D Sambora

ISBN 10: 1025112369 / ISBN 13: 9781025112367

Published by Creative Media Partners, LLC Mai 2025, 2025

About this Item

Neuware - 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.

Seller Inventory # 9781025112367

Bibliographic Details

• Title: Statistical Methods for Image Registration and Denoising
• Publisher: Creative Media Partners, LLC Mai 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 1025112369
• ISBN 13: 9781025112367
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 156H x 11D millimeters 234 millimeters width by 156 millimeters height by 11 millimeters depth
• Weight: 395 grams

About this title

Synopsis

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.

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