Abstract Infrared and visible images play an important role in transportation systems since they can monitor traffic conditions around the clock. However, visible images are susceptible to the imaging environments, and infrared images are not rich enough in detail. The infrared and visible images fusion techniques can fuse these two different modal images into a single image with more useful information. In this paper, we propose an effective infrared and visible images fusion method for traffic systems. The weight maps are measured by utilizing the sparse coefficients. The next is to decompose the infrared and visible pair into high-frequency layers (HFLs) and low-frequency layers (LFLs). Since the two layers contain different structures and texture information, to extract the representative component, the guided filter is utilized to optimize weight maps in accordance with the different characteristic of the infrared and visible pairs. The final step is to reconstruct the two-scale layers according to the weight maps. Experimental results demonstrate our method outperforms other popular approaches in terms of subjective perception and objective metrics.

中文翻译：

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使用稀疏表示和引导滤波器的红外和可见光图像融合

摘要 红外和可见光图像在交通系统中发挥着重要作用，因为它们可以全天候监测交通状况。然而，可见光图像易受成像环境的影响，红外图像细节不够丰富。红外和可见光图像融合技术可以将这两种不同的模态图像融合为一个具有更多有用信息的图像。在本文中，我们为交通系统提出了一种有效的红外和可见光图像融合方法。权重图是通过利用稀疏系数来测量的。接下来是将红外和可见光对分解为高频层 (HFL) 和低频层 (LFL)。由于两层包含不同的结构和纹理信息，为了提取代表成分，引导滤波器用于根据红外和可见光对的不同特性优化权重图。最后一步是根据权重图重建两尺度层。实验结果表明，我们的方法在主观感知和客观指标方面优于其他流行方法。