Deep sparse representation for robust image registration

Yeqing Li, Chen Chen, Fei Yang, Junzhou Huang
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
The definition 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 sparsified 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
more » ... exist on medical images, remotely sensed images and natural photos due to the difference of acquisition modalities or illumination conditions. Two efficient algorithms are proposed to solve the batch image registration and pair registration problems in a unified framework. We validate our method on extensive challenging datasets. The experimental results demonstrate the robustness, accuracy and efficiency of our method over 9 traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications.
doi:10.1109/cvpr.2015.7299123 dblp:conf/cvpr/LiCYH15 fatcat:pbac6vldqbh2xk4w6v65foqerm