Image Mosaicing and Super-resolution / Edition 1

Image Mosaicing and Super-resolution / Edition 1

by David Capel

ISBN-10: 1852337710

ISBN-13: 9781852337711

Pub. Date: 01/09/2004

Publisher: Springer London

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


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.

Product Details

Springer London
Publication date:
Distinguished Dissertations Series
Edition description:
Product dimensions:
5.98(w) x 9.02(h) x (d)

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.- 2.1.1 Registration by a geometric transformation.- 2.1.2 Ensuring global consistency.- 2.1.3 Other parametric surfaces.- 2.2 Image mosaicing.- 2.3 Super-resolution.- 2.3.1 Simple super-resolution schemes.- 2.3.2 Methods using a generative model.- 2.3.3 Super-resolution using statistical prior image models.- 3 Registration: Geometric and Photometric.- 3.1 Introduction.- 3.2 Imaging geometry.- 3.3 Estimating homographies.- 3.3.1 Linear estimators.- 3.3.2 Non-linear refinement.- 3.3.3 The maximum likelihood estimator of H.- 3.4 A practical two-view method.- 3.5 Assessing the accuracy of registration.- 3.5.1 Assessment criteria.- 3.5.2 Obtaining a ground-truth homography.- 3.6 Feature-based vs. direct methods.- 3.7 Photometric registration.- 3.7.1 Sources of photometric difference.- 3.7.2 The photometric model.- 3.7.3 Estimating the parameters.- 3.7.4 Results.- 3.8 Application: Recovering latent marks in forensic images.- 3.8.1 Motivation.- 3.8.2 Method.- 3.8.3 Further examples.- 3.9 Summary.- 4 Image Mosaicing.- 4.1 Introduction.- 4.2 Basic method.- 4.2.1 Outline.- 4.2.2 Practical considerations.- 4.3 Rendering from the mosaic.- 4.3.1 The reprojection manifold.- 4.3.2 The blending function.- 4.3.3 Eliminating seams by photometric registration.- 4.3.4 Eliminating seams due to vignetting.- 4.3.5 A fast alternative to median filtering.- 4.4 Simultaneous registration of multiple views.- 4.4.1 Motivation.- 4.4.2 Extending the two-view framework to N-views.- 4.4.3 A novel algorithm for feature-matching over N-views.- 4.4.4 Results.- 4.5 Automating the choice of reprojection frame.- 4.5.1 Motivation.- 4.5.2 Synthetic camera rotations.- 4.6 Applications of image mosaicing.- 4.7 Mosaicing non-planar surfaces.- 4.8 Mosaicing “user’s guide”.- 4.9 Summary.- 4.9.1 Further examples.- 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.- 5.4.1 A note on the assumptions made in the model.- 5.4.2 Discretization of the imaging model.- 5.4.3 Related approaches.- 5.4.4 Computing the elements in Mn.- 5.4.5 Boundary conditions.- 5.5 Justification for the Gaussian PSF.- 5.6 Synthetic test images.- 5.7 The average image.- 5.7.1 Noise robustness.- 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.- 5.11.1 Observation noise.- 5.11.2 Poorly estimated PSF.- 5.11.3 Inaccurate registration parameters.- 5.12 Irani and Peleg’s method.- 5.12.1 Least-squares minimization by steepest descent.- 5.12.2 Irani and Peleg’s algorithm.- 5.12.3 Relationship to the ML estimator.- 5.12.4 Convergence properties.- 5.13 Gallery of results.- 5.14 Summary.- 6 Super-resolution Using Bayesian Priors.- 6.1 Introduction.- 6.2 The Bayesian framework.- 6.2.1 Markov random fields.- 6.2.2 Gibbs priors.- 6.2.3 Some common cases.- 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.- 6.6.1 Exercising the prior models.- 6.6.2 Robustness to image noise.- 6.7 Hyper-parameter estimation by cross-validation.- 6.8 Gallery of results.- 6.9 Super-resolution “user’s guide”.- 6.10 Summary.- 7 Super-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.- 7.4.1 An ML estimator (FS-ML).- 7.4.2 MAP estimators.- 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.- 8.2.1 Application to digital video.- 8.2.2 Model-based super-resolution.- 8.3 Final observations.- A Large-scale Linear and Non-linear Optimization.- References.

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