MA: Low rank- and sparsity-based image registration
Image Registration Experiments
First simple experiments for non-parametric image registration, written in MATLAB. Provides:
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Distance Measures
- SSD
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Regularizers
- Diffusive Energy
- Curvature Energy
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Optimization schemes
- Gradient descent
- Gauß-Newton optimization
- Armijo line search
- Support for Multi-Level strategy
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Miscellaneous
- Derivative test (1st + 2nd order) for multivariate functions
Primal Dual Optimization
Convex optimization experiments with first-order primal-dual algorithm by Chambolle & Pock. Provides:
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TV-L1 Image Denoising
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TV-L2 and TV-L1 Image Registration
Note: Image Registration procedures use an iterative linear approximation of the image model to achieve a convex data term. Details can be found in A Duality Based Algorithm for TV-L1-Optical-Flow Image Registration.
Nuclear Norm Experiments
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A new distance measure for simultaneous image registration of an arbitrary number of template images (omitting a predefined reference). The rough idea is to constrain the rank of the matrix of column-major images, thus enforcing similarity between the images. Based on (and modified from) Shape from Light Field Meets Robust PCA.
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Optimization is performed in a similiar fashion as the TV-L1 and TV-L2 registration from above, i.e. using convex image model approximations and applying the primal-dual algorithm by Chambolle & Pock.
This project and all code included with it is licensed under the MIT Open Source Lincense (see LICENSE file for details). If you have questions, contact me at roland.haase [at] student.uni-luebeck [dot] de.