Overview

The Distinguished Dissertation Series is published on behalf of the Conference of Professors and Heads of Computing and the British Computer Society, who annually select the best British PhD dissertations in computer science for publication. The dissertations are selected on behalf of the CPHC by a panel of eight academics. Each dissertation chosen makes a noteworthy contribution to the subject and reaches a high standard of exposition, placing all results clearly in the context of computer science as a whole. In this way computer scientists with significantly different interests are able to grasp the essentials - or even find a means of entry - to an unfamiliar research topic. This book investigates how information contained in multiple, overlapping images of a scene may be combined to produce images of superior quality. This offers possibilities such as noise reduction, extended field of view, blur removal, increased spatial resolution and improved dynamic range. Potential applications cover fields as diverse as forensic video restoration, remote sensing, video compression and digital video editing. The book covers two aspects that have attracted particular attention in recent years: image mosaicing, whereby multiple images are aligned to produce a large composite; and super-resolution, which permits restoration at an increased resolution of poor quality video sequences by modelling and removing imaging degradations including noise, blur and spacial-sampling. It contains a comprehensive coverage and analysis of existing techniques, and describes in detail novel, powerful and automatic algorithms (based on a robust, statistical framework) for applying mosaicing and super-resolution. The algorithms may be implemented directly from the descriptions given here. A particular feature of the techniques is that it is not necessary to know the camera parameters (such as position and focal length) in order to apply them. Throughout the book, examples are given on real image sequences, covering a variety of applications including: the separation of latent marks in forensic images; the automatic creation of 360 panoramic mosaics; and super-resolution restoration of various scenes, text, and faces in lw-quality video.

Full Product Details

Author:   David Capel
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   2004 ed.
Dimensions:   Width: 15.20cm , Height: 1.40cm , Length: 22.90cm
Weight:   0.555kg
ISBN:  

9781852337711

ISBN 10:   1852337710
Pages:   218
Publication Date:   19 January 2004
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

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.- 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.- 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.

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