RIGID IMAGE REGISTRATION USING L2-NORM MINIMIZATION VIA COARSE SEARCH APPROACH

Abstract

Image registration is the process of aligning two or more images of the same scene captured under different conditions. It is widely used in applications such as medical imaging, computer vision, and remote sensing. In this study, we present a rigid image registration approach based on the minimization of an L2-norm objective function. The proposed method employs a coarse search algorithm to explore the parameter space of rigid transformations, including rotation, scaling, and translation. The objective function is defined as the squared difference between the transformed source image and the target image, which is minimized to achieve optimal alignment. The approach is evaluated on both synthetic data (where the target image is generated from the source) and non-synthetic data. The results demonstrate that the coarse search method is capable of achieving accurate registration in cases where the parameter space is adequately sampled. However, its performance depends on the resolution of the search grid. This work highlights the effectiveness of L2-norm-based objective functions in rigid image registration and demonstrates that coarse search can serve as a simple yet reliable optimization strategy.

Keywords : Image Registration, Rigid Transformation, L2 Norm, Coarse Search, Least Squares.