/MA

Master's thesis 'Low rank- and sparsity-based image registration'

Primary LanguageMATLABMIT LicenseMIT

MA: Low rank- and sparsity-based image registration

Image Registration Experiments

First simple experiments for non-parametric image registration, written in MATLAB. Provides:

  • Distance Measures

    • SSD

  • Regularizers

    • Diffusive Energy
    • Curvature Energy

  • Optimization schemes

    • Gradient descent
    • Gauß-Newton optimization
    • Armijo line search
    • Support for Multi-Level strategy

  • 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:

  • TV-L1 Image Denoising

  • 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

  • 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.

  • 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.