A new patch-based two-scale decomposition for infrared and visible image fusion
A new patch-based two-scale decomposition for infrared and visible image fusion
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一种新的基于补丁的红外和可见光图像融合的两尺度分解
DOI：
10.1016/j.infrared.2020.103362
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发表时间：
2020-07
影响因子：
3.3
通讯作者：
Xiaobing Dai
中科院分区：
文献类型：
--
作者：
Minghui Wu;Yong Ma;Jun Huang;Fan Fan;Xiaobing Dai
通常，在现有的两尺度融合方法中，源图像首先经过滤波器处理以获得两层，包括基础层和细节层。其次，应用特定的融合策略来融合这些层。这里，基础层的融合影响最终融合结果的对比度和清晰度，因为基础层可能包含大规模的强度变化。然而，由于基于滤波分解的基础层本身的局限性，融合结果的对比度很容易降低，甚至融合结果中源图像的细节可能会变得模糊。在本文中，提出了一种新的两尺度融合方法。在二尺度分解过程中，提出了一种新的基于块的低秩表示来获得源图像的两层。在所提出的分解方法中，基础层的表达从单个滤波分解转换为变换域。它由字典和相应的系数组成。在融合过程中，我们根据系数值设计全局统计融合规则来实现基础层融合，提高了最终融合结果的对比度。对于细节层融合，选择L1范数策略将细节层包含的小尺度特征整合到最终的融合结果中，该结果可以包含清晰的细节。实验结果表明，所提出的框架在公共数据库上优于传统方法。
Typically, in existing two-scale fusion method, source images are first processed by a filter to obtain two layers, including a base layer and a detail layer. Second, a specific fusion strategy is applied to fuse these layers. Herein, the fusion of the base layer influences the contrast and clearness of final fusion result because the base layer may contain large scale variations in intensity. However, due to the inherent limitation of the base layer based on the filtering decomposition, the contrast of fusion result is easily reduced, or even the detail of the source images, in the fusion result, may be blurred. In this paper, a new two-scale fusion approach is proposed. In the process of two-scale decomposition, a new patch-based low-rank representation is proposed to obtain the two layers of the source images. In the proposed decomposition approach, the expression of the base layer is converted from a single filtering decomposition to a transform domain. It consists of a dictionary and corresponding coefficients. In the process of fusion, we design a global statistical fusion rule based on the coefficient value to achieve the base layer fusion, which improves the contrast of the final fusion result. For detail layer fusion, the L1-norm strategy is chosen to integrate small scale features contained in the detail layer into the final fusion result, which can contain clear detail. Experimental results illustrate that the proposed framework outperforms traditional approaches on the public database.
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