基于GTF红外与可见光图像融合的分析与应用

Tingting Lv, Lei Zhang
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引用次数: 1

摘要

从1997年到2006年,中国铁路经历了6次大规模提速改造,部分提速速度达到250公里/小时。随着列车运行速度的提高,受电弓供电系统变得越来越重要。由于红外图像具有透光性,本文将红外图像与可见光图像进行融合,得到更多的受电弓区信息,使列车驾驶员能够更好地了解受电弓区情况。现有的融合方法通常对不同的源图像使用相同的表示,提取相似的特征。然而,它可能不适用于红外线和可见光图像。本文采用梯度转移融合(GTF)融合算法,可以同时保留热辐射和外观信息。为了证明GTF方法的有效性,从定量方面与其他17种融合算法进行了比较。此外,为了获得更好的融合效果,对GTF方法中的参数进行了分析和选择。最后,提出了一种改进GTF方法的彩色图像融合方法,该方法在融合图像中保留了可见图像的颜色信息,并在公开数据集上进行了测试,以证明其有效性。
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Analysis and Application Based on GTF Infrared and Visible Image Fusion
From 1997 to 2006, China's railway has undergone six large-scale speed-raising reconstruction, and some of the speed has reached 250KM/h. With the development of train speed, the pantograph-cantenary system becomes more and more important. Since infrared image has light penetration, this paper fuses infrared and visible images to get more information about the pantograph-cantenary so that train drivers can learn more about the pantograph-cantenary situation. Existing fusion methods typically use the same representations and extract the similar characteristics for different source images. However, it may don’t work for infrared and visible images. In this paper, we use the fusion algorithm named Gradient Transfer Fusion (GTF), which can keep the thermal radiation and appearance information simultaneously. To prove the effectiveness of the GTF method, it is compared with other 17 fusion algorithms from quantitative aspects. Furthermore, the parameter in the GTF method is analyzed and selected for better fusion results. Finally, color image fusion which is an improvement to the GTF method preserving the color information of the visible image in the fused image is proposed and it is tested on publicly available data sets to prove its availability.
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