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Image Mosaicing and Super-resolution

This book investigates sets of images consisting of many overlapping viewsofa scene, and how the information contained within them may be combined to produce single images of superior quality. The generic name for such techniques is frame fusion. Using frame fusion, it is possible to extend the fiel...

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Bibliographic Details
Main Author: Capel, David
Format: eBook
Language:English
Published: London Springer London 2004, 2004
Edition:1st ed. 2004
Series:Distinguished Dissertations
Subjects:
Image Processing / Digital Techniques
Computer Vision
Computer Imaging, Vision, Pattern Recognition And Graphics
Automated Pattern Recognition
Pattern Recognition Systems
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 7.3 Learning a face model using PCA
  • 7.4 Super-resolution using the PCA model
  • 7.5 The behaviour of the face model estimators
  • 7.6 Examples using real images
  • 7.7 Summary
  • 8 Conclusions and Extensions
  • 8.1 Summary
  • 8.2 Extensions
  • 8.3 Final observations
  • A Large-scale Linear and Non-linear Optimization
  • References
  • 5.2 What do we mean by “resolution”?
  • 5.3 Single-image methods
  • 5.4 The multi-view imaging model
  • 5.5 Justification for the Gaussian PSF
  • 5.6 Synthetic test images
  • 5.7 The average image
  • 5.8 Rudin’s forward-projection method
  • 5.9 The maximum-likelihood estimator
  • 5.10 Predicting the behaviour of the ML estimator
  • 5.11 Sensitivity of the ML estimator to noise sources
  • 5.12 Irani and Peleg’s method
  • 5.13 Gallery of results
  • 5.14 Summary
  • 6 Super-resolution Using Bayesian Priors
  • 6.1 Introduction
  • 6.2 The Bayesian framework
  • 6.3 The optimal Wiener filter as a MAP estimator
  • 6.4 Generic image priors
  • 6.5 Practical optimization
  • 6.6 Sensitivity of the MAP estimators to noise sources
  • 6.7 Hyper-parameter estimation by cross-validation
  • 6.8 Gallery of results
  • 6.9 Super-resolution “user’s guide”
  • 6.10 Summary
  • 7Super-resolution Using Sub-space Models
  • 7.1 Introduction
  • 7.2 Bound constraints
  • 1 Introduction
  • 1.1 Background
  • 1.2 Modelling assumptions
  • 1.3 Applications
  • 1.4 Principal contributions
  • 2 Literature Survey
  • 2.1 Image registration
  • 2.2 Image mosaicing
  • 2.3 Super-resolution
  • 3 Registration: Geometric and Photometric
  • 3.1 Introduction
  • 3.2 Imaging geometry
  • 3.3 Estimating homographies
  • 3.4 A practical two-view method
  • 3.5 Assessing the accuracy of registration
  • 3.6 Feature-based vs. direct methods
  • 3.7 Photometric registration
  • 3.8 Application: Recovering latent marks in forensic images
  • 3.9 Summary
  • 4 Image Mosaicing
  • 4.1 Introduction
  • 4.2 Basic method
  • 4.3 Rendering from the mosaic
  • 4.4 Simultaneous registration of multiple views
  • 4.5 Automating the choice of reprojection frame
  • 4.6 Applications of image mosaicing
  • 4.7 Mosaicing non-planar surfaces
  • 4.8 Mosaicing “user’s guide”
  • 4.9 Summary
  • 5 Super-resolution: Maximum Likelihood and Related Approaches
  • 5.1 Introduction

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