Image Mosaicing and Super-resolution

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. Language: English