Abstract

The defifinition of the similarity measure is an essential component in image registration. In this paper, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by that the optimally registered images can be deeply sparsifified in the gradient domain and frequency domain, with the separation of a sparse tensor of errors. One of the key advantages of the proposed similarity measure is its robustness to severe intensity distortions, which widely exist on medical images, remotely sensed images and natural photos due to the difference of acquisition modalities or illumination conditions. Two effificient algorithms are proposed to solve the batch image registration and pair registration problems in a unifified framework. We validate our method on extensive challenging datasets. The experimental results demonstrate the robustness, accuracy and effificiency of our method over 9 traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications