Robust landmark-based image registration using l1 and l2 norm regularizations

In landmark-based image registration, estimation of transformation based on radial basis functions (RBFs) expansions has been successfully utilized in many applications. A novel landmark-based image registration method regularized by l(1) and l(2) norm is proposed in this paper to estimate transformations based on corresponding landmarks. The compact supported radial basis functions (CSRBFs) are utilized in our method. To estimate the CSRBFs coefficients of transformations, we construct a linear model and respectively regularize the elastic and affine deformation coefficients by l(1) and l(2) norm. Experiments show that the transformations estimated by our method are robust to noised correspondences of landmarks, the bending energy of transformations is less and topology of the deformation field can be preserved better than existing other methods.