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Hierarchical Sparse Representation for Robust Image Registration | IEEE Journals & Magazine | IEEE Xplore

Hierarchical Sparse Representation for Robust Image Registration


Abstract:

Similarity measure is an essential component in image registration. In this article, we propose a novel similarity measure for registration of two or more images. The pro...Show More

Abstract:

Similarity measure is an essential component in image registration. In this article, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by the fact that optimally registered images can be sparsified hierarchically in the gradient domain and frequency domain with the separation of sparse errors. One of the key advantages of the proposed similarity measure is its robustness in dealing with severe intensity distortions, which widely exist on medical images, remotely sensed images and natural photos due to differences 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 have validated our method on extensive and challenging data sets. The experimental results demonstrate the robustness, accuracy and efficiency of our method over nine traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications.
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 40, Issue: 9, 01 September 2018)
Page(s): 2151 - 2164
Date of Publication: 01 September 2017

ISSN Information:

PubMed ID: 28880157

Funding Agency:


References

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