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Adaptive structured dictionary learning for image fusion based on group-sparse-representation
Author(s): Jiajie Yang; Bin Sun; Chengwei Luo; Yuzhong Wu; Limei Xu
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Paper Abstract

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a ℓ1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Paper Details

Date Published: 10 April 2018
PDF: 11 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061535 (10 April 2018); doi: 10.1117/12.2304585
Show Author Affiliations
Jiajie Yang, Univ. of Electronic Science and Technology of China (China)
Bin Sun, Univ. of Electronic Science and Technology of China (China)
Chengwei Luo, Univ. of Electronic Science and Technology of China (China)
Yuzhong Wu, Univ. of Electronic Science and Technology of China (China)
Limei Xu, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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