Abstract Infrared imaging sensors are widely employed in urban traffic systems since they are not affected by lighting conditions. However, due to the limitation of hardware and imaging environment, it is difficult to obtain infrared (IR) images at the desired quality. IR images always lack detailed information, which leads to unsatisfying IR image enhancement results with the conventional method. Compared with the IR images, the visible (VIS) images contain detailed information, which could help to enhance the quality of the corresponding IR images. In this article, we propose an effective method to enhance IR images by applying the multi-sensors image. First, we adopt the edge-preserving filter to decompose the IR and VIS images into illumination and reflectance components according to Retinex theory. Second, each region in the IR and VIS image is classified into the related region and non-related region according to the correlation between IR and VIS images. Finally, an adaptive fuzzy plateau HE (AFPHE) is utilized to enhance the illumination component, and a strategy is employed to enhance the detail of the IR reflectance component with the help of VIS images. Experimental results demonstrate that the proposed method can effectively improve the contrast and enhance the detail of the IR images.

中文翻译：

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基于可见光图像相关性测量的城市交通监控系统红外图像增强新方法

摘要 红外成像传感器由于不受光照条件的影响而被广泛应用于城市交通系统中。然而，由于硬件和成像环境的限制，很难获得所需质量的红外（IR）图像。红外图像总是缺乏细节信息，这导致传统方法的红外图像增强效果不尽如人意。与红外图像相比，可见光（VIS）图像包含详细信息，有助于提高相应红外图像的质量。在本文中，我们提出了一种通过应用多传感器图像来增强红外图像的有效方法。首先，我们采用边缘保留滤波器根据 Retinex 理论将 IR 和 VIS 图像分解为光照和反射分量。第二，IR和VIS图像中的每个区域根据IR和VIS图像之间的相关性分为相关区域和非相关区域。最后，利用自适应模糊高原HE（AFPHE）来增强照明分量，并采用一种策略在VIS图像的帮助下增强IR反射分量的细节。实验结果表明，所提出的方法可以有效提高红外图像的对比度并增强其细节。