基于对数域分解和信息交互的红外与可见光图像融合

Erfang Fei, Zhiqiang Zhou, Rao Yang, Lingjuan Miao
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摘要

为了更好地保留红外和可见光图像的显著目标和细节信息,本文提出了一种基于对数域分解和信息交互的图像融合方法。具体而言,首先将红外和可见光图像转换到对数域进行双尺度分解,与直接在图像空间中分解相比,这有助于提取更多的高对比度信息。然后使用视觉显著性策略融合基础层图像。对于细节层,提出了局部视觉显著性和细节保留相结合的策略来确定最终的融合权值。此外,值得注意的是,在融合前将可见光图像信息引入红外细节层,实现了两源图像的信息互补和交互。实验结果表明,该方法在定性和定量评价方面都优于其他融合方法。
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Infrared and Visible Image Fusion based on Log-domain Decomposition and Information Interaction
In order to better preserve the salient targets and detail information of the infrared and visible images, a novel image fusion method based on log-domain decomposition and information interaction is proposed in this paper. Specifically, the infrared and visible images are first transformed into the logarithmic domain for a two-scale decomposition, which helps to extract more high-contrast information compared to decomposing them directly in image space. A visual saliency strategy is then used to fuse the base layer images. As to detail layers, a combined local visual saliency and detail preservation strategy is proposed to determine the final fusion weights. In addition, it is worth noting that the visible image information is introduced into the infrared detail layer before fusion, which achieves the information complementation and interaction of two source images. The experiment results demonstrate that the proposed method outperforms other fusion methods in both qualitative and quantitative assessments.
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