10 April 2018 Adaptive structured dictionary learning for image fusion based on group-sparse-representation
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061535 (2018) https://doi.org/10.1117/12.2304585
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
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.
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Jiajie Yang, Jiajie Yang, Bin Sun, Bin Sun, Chengwei Luo, Chengwei Luo, Yuzhong Wu, Yuzhong Wu, Limei Xu, Limei Xu, } "Adaptive structured dictionary learning for image fusion based on group-sparse-representation", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061535 (10 April 2018); doi: 10.1117/12.2304585; https://doi.org/10.1117/12.2304585
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