A Full Curvature Based Algorithm for Image Registration
Journal of Mathematical Imaging and Vision
Multiscale Joint Segmentation and Registration of Image Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Rigid and Generalized Affine Image Registration and Interpolation by Geometric Multigrid
Journal of Mathematical Imaging and Vision
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
An Optimal Control Formulation of an Image Registration Problem
Journal of Mathematical Imaging and Vision
Pattern recognition methods for thermal drift correction in Atomic Force Microscopy imaging
Pattern Recognition and Image Analysis
A robust multigrid approach for variational image registration models
Journal of Computational and Applied Mathematics
A variational image registration approach based on curvature scale space
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
A scale space method for volume preserving image registration
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
| Hi-index | 0.00 |
In this paper, we consider the problem of matching images, i.e., to find a deformation u, which transforms a digital image into another such that the images have nearly equal gray values in every image element. The difference of the two images is measured by their L2-difference, which should be minimized. This yields a nonlinear ill conditioned inverse problem for u, so the numerical solution is quite difficult. A Tikhonov regularization method is considered to rule out discontinuous and irregular solutions to the minimization problem. An important problem is a proper choice of the regularization parameter $\alpha$. For the practical choice of $\alpha,$ we use iterative regularization methods based on multigrid techniques. To obtain a suitable initial guess, we use an approach similar to the full multigrid (FMG) developed by Brandt [Math. Comp., 31 (1977), pp. 333--390]. The algorithms have optimal complexity: the amount of work is proportional to the number of picture elements. Finally, we present some experimental results for synthetic and real images.