Title :
Vectorial Total Variation-Based Regularization for Variational Image Registration
Author_Institution :
Dept. of Math., Silpakorn Univ., Nakorn Pathom, Thailand
Abstract :
To use interdependence between the primary components of the deformation field for smooth and non-smooth registration problems, the channel-by-channel total variation- or standard vectorial total variation (SVTV)-based regularization has been extended to a more flexible and efficient technique, allowing high quality regularization procedures. Based on this method, this paper proposes a fast nonlinear multigrid (NMG) method for solving the underlying Euler-Lagrange system of two coupled second-order nonlinear partial differential equations. Numerical experiments using both synthetic and realistic images not only confirm that the recommended VTV-based regularization yields better registration qualities for a wide range of applications than those of the SVTV-based regularization, but also that the proposed NMG method is fast, accurate, and reliable in delivering visually-pleasing registration results.
Keywords :
image registration; nonlinear differential equations; partial differential equations; Euler-Lagrange system; SVTV-based regularization; fast nonlinear multigrid method; nonsmooth registration problems; primary components; second-order nonlinear partial differential equations; smooth registration problems; standard vectorial total variation-based regularization; variational image registration; Approximation methods; Image registration; Imaging; Materials; Minimization; Standards; Deformable image registration; inverse problems; nonlinear multigrid; regularization; variational image registration; vectorial total variation; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2013.2274749