Non-rigid infrared and visible image registration by enhanced affine transformation

Abstract

Image registration is a prerequisite for infrared (IR) and visible (VIS) image fusion. In practical application, most scenes are not planar and there is significant distinctness between IR and VIS cameras. Therefore, for non-rigid IR and VIS image registration, non-linear transformation is more applicable than affine transformation. Typically, non-linear transformation is modeled with point feature. However, this can degrade the generalization ability of transformation model and increase computational complexity. Aim at this problem, we propose an enhanced affine transformation (EAT) for non-rigid IR and VIS image registration. In this paper, image registration is transformed into point set registration and then the optimal EAT model constructed by global deformation is estimated from local feature. At first, a Gaussian-fields-based objective function is established and simplified by using the potential correspondence between an image pair. With the combination of affine and polynomial transformation, the EAT model is then proposed to describe the regular pattern of non-rigid and global deformation between an image pair. Finally, a coarse-to-fine strategy based on quasi-Newton method is designed and applied to determine the optimal transformation coefficients from edge point feature of IR and VIS images, in order to accomplish non-rigid image registration. The qualitative and quantitative comparisons on synthesized point sets and real images demonstrate that the proposed method is superior over the state-of-the-art methods in the accuracy and efficiency of image registration.