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.- 2.1.1 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-sc... Mehr