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Adaptive Structured Dictionary Learning for Image Fusion Based on Group-Sparse-Representation

机译：基于群体稀疏表示的图像融合自适应结构字典学习

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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 ℓ 1-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.
机译：字典学习是稀疏表示的关键过程，它是图像融合中使用最广泛的图像表示理论之一。现有的字典学习方法不能很好地利用群体结构信息和稀疏系数。在本文中，我们提出了一种新的自适应结构化字典学习算法和ℓ1-范数最大融合规则，该规则创新地利用分组稀疏系数来合并图像。在字典学习算法中，我们不需要有关字典的任何组结构的先验知识。通过使用字典中表示信号的特性，我们的算法可以自动找到隐藏在字典中的所需潜在结构信息。融合规则具有组结构字典的物理含义，并且在合并图像时对结构信息进行活动级别的判断。因此，融合图像可以保留更多重要信息。已与几种最先进的词典学习方法和融合规则进行了比较。实验结果表明，在几种客观评价指标上，字典学习算法和融合规则均优于其他算法。

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