Multi-Modal Registration Using a Block-Wise Coarse-to-Fine Approach

Multi-modal registration is a fundamental problem in medical imaging, remote sensing and computer vision applications. Moreover, it is significantly challenging task as compared to mono-modal registration due to the complicated relationships between various modalities. Conventional approaches for registering multi-modal images suffer from limited correct matched points, high computational cost and poor registration accuracy. In this paper, we propose a multi-modal registration method using block-wise processing and coarse-to-fine approach. The proposed method efficiently extracts larger number of distinctive key-points and consequently provides better registration accuracy. In addition, it is able to handle weak illumination conditions effectively. In order to evaluate the effectiveness of the proposed method, a number of experiments are carried out using well-known datasets. The performance of the proposed scheme is then compared with the state-of-the-art methods using precision qualitative measure. The comparative analysis demonstrates that the proposed method has achieved higher precision values as compared to the obtained from conventional methods. In addition, the proposed scheme has provided more accurate registration results through lower computational complexity.