Image Mosaicing and Super-resolution - Capel, David

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Résumé :
A comprehensive analysis of techniques used in improving the quality of video images...

Sommaire:
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.